Daniela Zaharie -...

Daniela Zaharie

Date contact Departamentul de InformaticăFacultatea de Matematică şi InformaticăUniversitatea de Vest din Timişoara +40 729 639 576blvd. V. Pârvan, 4 [email protected], Timişoara, Romania

Data şi loculnaşterii

1 septembrie 1965, Arad

Domenii deinteres

Algoritmi evolutivi, reţele neuronale artificiale, analiza datelor, prelucrarea imaginilor,calcul paralel şi distribuit, statistică computaţională, modele computaţionale ı̂nbiologie

Educaţie Facultatea de Matematică şi Informatică, Universitatea de Vest din Timişoara,Romania

Doctorat, Matematică (Teoria Probabilităţilor şi Statistică, 1992-1997

• Titlul tezei: Modelări stohastice ale reţelelor neuronale şi aplicaţii• Coordonator: prof.dr. Gheorghe Constantin

Licenţa, Matematică, specializarea Informatică, 1987 (şefă de promoţie)

Experienţăprofesională

Profesor din 2009Departamentul de Informatică, Facultatea de Matematică şi Informatică, Universitateade Vest din Timişoara

Conferenţiar 1999 - 2009Departamentul de Informatică, Facultatea de Matematică şi Informatică, Universitateade Vest din Timişoara 2001 - 2009

Catedra de Teoria Probabilităţilor şi Matematici Aplicate, Facultatea de Matematicăşi Informatică, Universitatea de Vest din Timişoara 1999 - 2001

Lector 1992 - 1999Catedra de Teoria Probabilităţilor şi Matematici Aplicate, Facultatea de Matematicăşi Informatică, Universitatea de Vest din Timişoara

Asistent 1990-1992Catedra de Informatică, Facultatea de Matematică şi Informatică, Universitateade Vest din Timişoara

Analist-programator 1987 - 1990Centrul de Calcul, IAEM Timisoara

Activitatedidactică • Cursuri nivel licenţă: Algoritmi şi structuri de date (2015-prezent), Algoritmică

(2003-2014), Algoritmi şi programare (1993-2001), Reţele neuronale (1996-2007),Statistică (1996-1999), Calcul ştiinţific (1999-2000), Introducere ı̂n informatică(1993-1996), Inteligenţă artificială (1993-1996).

• Cursuri nivel master: Data mining (română şi engleză, 2015-prezent), Algoritmimetaeuristici (română şi engleză, 2015-prezent), Calcul neuronal şi calcul evolutiv(română şi engleză, 2007-2014), Biostatistică şi bioinformatică (2007-2016).

Poziţiiconsultative şiadministrative

prodecan la Facultatea de Matematică şi Informatică din 2016

membru al Senatului Universităţii de Vest din Timişoara 2012 - 2015

membru ı̂n Consiliul Facultăţii de Matematică şi Informatică 2004-2015

1 of 43

mailto:[email protected]://www.uvt.rohttp://math.uvt.ro/http://math.uvt.ro/http://math.uvt.ro/

responsabil al programului de studii de licenţă Informatică Aplicată

din 2008

membru CNATDCU, comisia Informatică 2011 - 2012, 2015 - 2016

secretar ştiinţific la Departamentul de Informatică 2004 - 2012

Activităţieditoriale

Co-editor:• Analele Universităţii de Vest din Timişoara (seria Matematică şi Informatică)• seria SYNASC (Proceedings of the Symposium of Symbolic and Numeric Algorithms

for Scientific Computing)Membru al comitetului editorial:• Soft Computing. A Fusion of Foundations, Methodologies and Applications• Swarm and Evolutionary Computing

Activităţi deevaluare şirecenzare

Reviste: IEEE Transactions on Evolutionary Computing, IEEE Transactions onSystems, Man and Cybernetics, Applied Soft Computing, Information Sciences,Applied Mathematics and Computation, Soft Computing, Neurocomputing, MemeticComputing, Journal of Global Optimization, Computational Intelligence, EvolutionaryComputation, Computational Optimization and Applications, Patterns Analysisand Applications, Central European Journal on Computer Science etc.Conferinţe: cel puţin 10 conferinţe internaţionale/an (incluzând CEC, GECCO,FOGA, ANTS, IEEE SMC, IEEE SSCI, HAIS, NICSO, NaBIC, SOCO, FedCSIS,SACI, CompStat, IADIS-Data Mining etc.)Evaluare activitate de cercetare:• Evaluare aplicaţii submise la ANCS• Evaluare aplicaţii submise la National Research Foundation from South Africa• Evaluare dosare pentru poziţii academice (10 la universităţi din Romania; 1

pentru Budapest Univesity of Technology and Economics, Hungary; 1 pentruRobert Gordon University, Aberdeen, UK; 2 pentru Arab Open University,Kuwait)

Evaluare teze de doctorat: membru ı̂n comisie la 46 teze din Romania (de la 6universităţi), 4 teze din Franţa, 5 teze din Finlanda, o teză din Olanda, o teză dinSpania şi o theză din Africa de Sud.

Prezentăriinvitate

• Summer School on Image Processing, SSIP 2016, Szeged, Hungary, 7-16 iulie2016

• Workshop on Stochastic Geometry and Big Data, INRIA - Sophia Antipolis, 24noiembrie, 2015

• Scientific seminar of the Computer Vision Center, University Autonoma deBarcelona, 18 aprilie, 2013

• 18th International Conference on Soft Computing, Brno, 27-29 iunie, 2012• IDEAS Research Seminar, Robert Gordon University Aberdeen, 17 mai, 2011• Doctoral Summer School ”Evolutionary Computing Optimization and Data Mining”,

Iasi, 2010-2014

Premii Premiul ”Professor Zdzislaw Pawlak Best Paper Award” acordat de Polish InformationProcessing Society in cadrul simpozionului ”Advances in Artificial Intelligence andApplications (AAIA)”, 2007

Proiecte decercetare

• RO-PN - II: NatComp - New natural computing models for complexity andcomplex problems solving, 2007-2009 (responsabil echipa de cercetare)

• RO-CEEX: MaternQual - Integrated system for complex evaluation of risk factorsand prediction in obstetrics, 2006-2008 (responsabil echipa de cercetare)

• H2020: SESAME Net - Supercomputing Expertise for Small And Medium Enterprises,H2020-EINFRA, 2015-2017

2 of 43

• H2020: VI-SEEM - a unified Virtual Research Environment for South EasternEurope and Eastern Mediterranean, H2020-EINFRA, 2015-2017

• FP7: HOST - High Performance Computing Service Center, 2012-2014• FP7: HP-SEE - High-Performance Computing Infrastructure for South East

Europe’s Research Communities, 2010 - 2012• FP7: SPRERS - Strengthening the Participation of Romania at European R&D

in Software Services, 2010-2011• ESA-Pecs: GiSHEO - On Demand Grid Services for Higher Education and

Training in Earth Observation, 2009-2010• RO-PNII-ID-PCE: AMICAS - Automated Management in Cloud and Sky Computing

Environments, 2012-2016• RO-PNII-ID-PCE: REVISAL - Modelling and simulation of the dynamics of

thymocyte populations and the cellular components of medulla under normaland pathological conditions, 2012-2016

• RO-PN - II: Asistsys - Integrated system for patients with severe neuromotorproblems, 2009-2011

• RO-CEEX: GRIDMOSI - Virtual organization based on Grid technology for highperformance modelling, simulation and optimization, 2005 -2008

• RO-CEEX: MedioGrid - Parallel and distributed graphical processing on Gridinfrastructure of geographical and environmental data, 2005 -2008

• RO-CEEX: SIAPOM - Integrated system for analysis and optimal multi-disciplinarydesign, 2006-2008

• RO-CNCS, ViaSan: Bio-View software for simulating biological processes, 2004-2005

• RO-CNCS: Stochastic modelling, approximation theory and numerical approacheswith applications in turbulence and economy, 2002-2004

Publicaţii • 27 lucrări ı̂n reviste• 60 lucrări ı̂n volume ale conferinţelor• 2 capitole de carte• lista publicaţiilor este ı̂n Anexa A

Citări • cca 854 citări in SCOPUS• cca 1300 citări in Scholar Google• o listă a citărilor pentru o selecţie de lucrări este in Anexa B

Limbaje deprogramare

• C, C++, Java, Pascal, Python, Lisp, Fortran, Matlab, Mathematica, R

Limbi străine • engleza (mediu), franceza (̂ıncepător)

Asociaţiiprofesionale

Membru:• Romanian Association for Artificial Intelligence• IEEE CIS Task Force on Differential Evolution

3 of 43

Anexa A. Publicaţii (D. Zaharie)

Lucrări ı̂n reviste:

1. D. Zaharie, R.D. Moleriu, F.A. Mic - Modeling the development of the post-natal mouse thymus in the absence ofbone marrow progenitors, Scientific Reports 6, Article number: 36159 (2016), doi:10.1038/srep36159, 2016.

2. J. Li, A. Agathos, D. Zaharie, J. M. Bioucas-Dias, A. Plaza, X. Li - Minimum Volume Simplex Analysis: A FastAlgorithm for Linear Hyperspectral Unmixing, IEEE Transactions on Geoscience and Remote Sensing, Volume:53,Issue: 9, Pages 5067 - 5082, 2015.

3. R.D. Moleriu, D. Zaharie, L.C. Moatar-Moleriu, A.T. Gruia, A.A. Mic, F.A. Mic - Insights into the mechanisms ofthymus involution and regeneration by modeling the glucocorticoid-induced perturbation of thymocyte populationsdynamics, Journal of Theoretical Biology, Volume 348, Pages 80-99, 2014.

4. G. Iuhasz, V. Negru, D. Zaharie - Neuroevolution based multi-agent system with ontology based template creationfor micromanagement in real-time strategy games, Information Technology and Control, Volume 43, Issue 1, pp.98-109, 2014.

5. D. Petcu, S. Panica, M. Frncu, M. Neagul, D. Zaharie, G. Macariu, D. Gorgan, T. Stefanut - Experiences inbuilding a Grid-based platform to serve Earth observation training activities , Computer Standards & Interfaces,Volume 34, Issue 6, Pages 493-508, 2012.

6. D. Petcu, S. Panica, M. Neagul, M. Frincu, D. Zaharie, R. Ciorba, A. Dinis - Earth observation data processingin distributed systems, Informatica. An International Journal of Computing and Informatics, Slovenian SocietyInformatika, vol. 34 (4), pg. 463-476, 2010.

7. F. Zamfirache, D. Zaharie, C. Craciun - Nature inspired metaheuristics for task scheduling in static and dynamiccomputing environments, Scientific Bulletin of Politehnica University of Timisoara, Transactions on AutomaticControl and Computer Science, vol 55(69), nr. 3, pp. 133-142, 2010

8. D. Zaharie - Influence of crossover on the behavior of the Differential Evolution Algorithm, Applied Soft Computing,vol 9, issue 3, pg. 1126-1138, 2009.

9. C. Chira, A. Gog, D. Zaharie, D. Dumitrescu - Complex Dynamics in a Collaborative Evolutionary Search Model,Creative Mathematics and Informatics, vol. 17, no. 3, pg. 346-356, 2008.

10. D. Petcu, D. Gorgan, F. Pop, D. Tudor, D. Zaharie - Satellite Image Processing on a Grid-Based Platform,International Journal of ”Computing”, 2008, Vol. 7, Issue 2, pg. 51-58, 2008.

11. D. Lungeanu, D. Zaharie, S. Holban, E. Bernad, M. Bari, R. Noaghiu - Exploratory Analysis of Medical CodingPractices: the Relevance of Reported Data Quality in Obstetrics-Gynaecology, in Stud Health Technol Inform.Amsterdam: IOS Press, ISBN 978-1-58603-864-9, ISSN 0926-9630, pg. : 839-844, 2008.

12. N. Bonchis, St. Balint, D. Zaharie - The Ramsey optimal growth model on finite horizon. An. Univ. Vest Timis.,Ser. Mat.-Inform. 45, No. 1, 59-76, 2007.

13. D. Zaharie - Extensions of Differential Evolution Algorithms for Multimodal Optimization, Analele Univ. Timisoaravol. XLII, ISSN 1224-970X, Timisoara, pg. 331-345, 2004.

14. C. Jichici, V. Negru, D. Pop, D. Zaharie, H. Popa - A Predictive Model for Learning Objects Quality Evaluation, Sci. Ann. Cuza Univ. 15, Iasi, pp.153-166, 2004.

15. D. Zaharie - Multi-objective Optimization with Adaptive Pareto Differential Evolution, in Memoriile SectiilorStiintifice, Seria IV, Tomul XXVI, pp. 223-239, Ed. Academiei Romane, 2003.

16. D. Zaharie - Parameter Adaptation in Differential Evolution by Controlling the Population Diversity, Analele Univ.Timisoara, vol. XXXX, issue 2, 2002.

17. D. Zaharie - On the Explorative Power of Differential Evolution Algorithms, Analele Univ. Timisoara, vol. XXXIX,pp.249-260, 2001

18. D. Zaharie - On an evolutionary algorithm for global optimization, Analele Univ.Timisoara, vol. XXXVIII, fasc.2, pg. 203-216, 2000.

19. D. Zaharie - Iterated morphological operators implemented through cellular neural networks, Analele Univ.Timisoara,vol. XXXVII (special issue on Computer Science), pg. 169-178, 1999.

20. O. Francois, D. Zaharie - Markovian Perturbations of Discrete Iterations. Lyapunov Functions, global minimizationand associative memory, Mathematical and Computer Modelling, 29, pp. 81-94, 1999.

21. D.Zaharie - Some Properties of Binary Stochastic Networks, Analele Universitatii din Bucuresti, 1999.

22. D. Zaharie - A Class of Adaptive Recurrent Neural Networks and Image Enhancement, Analele St. ale UniversitatiiAl. I. Cuza, Iasi, seria Informatica, tom VIII, pg. 177-185, 1998.

23. D. Zaharie - On Stochastically Perturbed Differential Systems, Annali dell’Universita di Ferrara, Sez. VII, Sc.Mat.,vol. XLII, pp. 77-85, 1996.

24. D. Zaharie - A Markovian Study of Recurrent Neural Networks with Stochastic Dynamics, Informatica, LithuanianAcademy of Science, Vilnius, vol. 7, nr. 2, pp. 255-266, 1996.

25. D. Zaharie - Learning Algorithms for Feedback Neural Networks with Stochastic Dynamics, Analele Universitatiidin Timisoara, vol. XXXIII, fasc. 2, seria Matematica-Informatica, pp. 287-299, 1995.

4 of 43

26. D. Zaharie - On the Stochastic Modelling of Feedback Neural Networks, Analele Universitatii din Timisoara, vol.XXXI, fasc. 2, seria Matematica-Informatica, pp. 281-297, 1993.

27. D. Zaharie - On the Models of Random Automata, Analele Universitatii din Timisoara, vol. XXVIII, fasc. 2-3,seria Stiinte Matematice, pp. 211-222, 1990.

Capitole de cărţi

1. D. Zaharie, D. Lungeanu, F. Zamfirache - Interactive Search of Rules in Medical Data Using MultiobjectiveEvolutionary Algorithms, chapter in Genetic and Evolutionary Computation: Medical Applications, Stephen Smithand Stefano Cagnoni (eds), John Wiley & Sons, pp 133-148, 2010

2. D. Petcu, D. Zaharie, M. Neagul, S. Panica, M. Frincu, D. Gorgan, T. Stefanut and V. Bacu - Remote SensedImage Processing on Grids for Training in Earth Observation , in Yung-Sheng Chen (Ed.), Image Processing,INTECH, chapter 8, pp 115-140, 2009.

Lucrări ı̂n volume ale conferinţelor

1. M. Erascu, F. Micota, D. Zaharie - A scalable hybrid approach for applications placement in the cloud, 2015 Confer-ence on Grid, Cloud & High Performance Computing in Science (ROLCG), 28-30 October 2015, 10.1109/ROLCG.2015.7367232,2015.

2. R. Dogaru, F. Micota, D. Zaharie - Searching for Taxonomy-based Similarity Measures for Medical Data, 7thBalkan Conference in Informatics, BCI 2015, 2-4 September, DOI:10.1145/2801081.2801102, 2015.

3. R. Dogaru, F. Micota, D. Zaharie - Taxonomy-based dissimilarity measures for profile identification in medicaldata , SISY 2015, 10.1109/SISY.2015.7325369, pg. 149-154, 2015.

4. L. Moatar-Moleriu, V. Negru, D. Zaharie - Evolutionary Estimation of Parameters in Computational Models ofThymocyte Dynamics, LSSC’13, June, Sozopol, Springer LNCS 8353, pg. 264-271, 2014.

5. D. Zaharie, L. Moatar-Moleriu, V. Negru - Particularities of Evolutionary Parameter Estimation in Multi-stageCompartmental Models of Thymocyte Dynamics, GECCO’13, July 6-10, Amsterdam, pg. 303-310, 2013.

6. G. Iuhasz, V. Negru, D. Zaharie - Neuroevolution based multi-agent system for micromanagement in real-time strat-egy games, Proceedings of the Fifth Balkan Conference in Informatics, pg 32-39, ACM doi:10.1145/2371316.2371324,2012

7. D. Zaharie - Differential Evolution: from Theoretical Results to Practical Insights, in Proc. of 18th InternationalConference on Soft Computing, June 27-29, Brno, Czech Republic, pg. 126-131, 2012.

8. A.C. Zvoianu, G. Kronberger, M. Kommenda, D. Zaharie, M. Affenzeller - Improving the Parsimony of RegressionModels for an Enhanced Genetic Programming Process, LNCS 6927, pp. 264-271, 2012.

9. D. Petcu, D. Zaharie, S. Panica, A.S. Hussein, A. Sayed; H. El-Shishiny, Fuzzy clustering of large satellite imagesusing high performance computing, Proc. SPIE 8183, High-Performance Computing in Remote Sensing, 818302(12 October 2011); doi: 10.1117/12.898281, 2011

10. D. Zaharie, L. Perian, V. Negru - A View Inside the Classification with Non-Nested Generalized Exemplars, IADISEuropean Conference on Data Mining, 24-26 July, Rome, Italy, pg.19-26, 2011

11. D. Zaharie, L.Perian, V. Negru, F. Zamfirache - Evolutionary pruning of non-nested generalized exemplars, inProc.of the 6th IEEE International Symposium on Computational Intelligence and Informatics, Timisoara, 19-21mai 2011, pg 57-62, 2011.

12. F. Zamfirache, M. Frincu, D. Zaharie - Population based Metaheuristics for Tasks Scheduling in HeterogenousDistributed Systems, in I.Dimov, S. Dimova, N. Kolkovska (eds), Proc. of NMA 2010 Conference, LNCS 6046, pg.321-327, 2011.

13. D. Dumitrescu, R. Lung, R. Nagy, D. Zaharie, A. Bartha, D. Logofatu - Evolutionary Detection of New Classesof Equilibria: Application in Behavioral Games. PPSN (2)’2010. pp.432 441, 2010

14. D. Dumitrescu, R. Lung, R. Nagy, D. Zaharie, A. Bartha - Exploring evolutionary detected fuzzy equilibria: A linkbetween normative theory and real life, Proceedings of the 12th Annual Genetic and Evolutionary ComputationConference, GECCO ’10 , pp. 539-540, 2010

15. F. Zamfirache, D. Zaharie, C. Craciun - Evolutionary task scheduling in static and dynamic environments, ICCC-CONTI 2010 - IEEE International Joint Conferences on Computational Cybernetics and Technical Informatics,Proceedings , art. no. 5491336, pp. 619-624, 2010

16. I.Zaharie, D. Zaharie - On The Non-Isothermal Crystallization of Fe60Gd5Cr15B20 Amorphous Alloys, in A.Angelopoulos, T. Fildisis (eds), AIP Conference Proceedings Volume 1203, 7th International Conference of theBalkan Physical Union pp. 335-340, 2010

17. C. Craciun, M. Nicoara, D. Zaharie, Enhancing the Scalability of Metaheuristics by Cooperative Coevolution, inI. Lirkov, S. Margenov, and J. Wasniewski (Eds.): Proc. of LSSC 2009, LNCS 5910, pp. 310-317, 2010.

18. M. Neagul, S. Panica, D. Petcu, D. Zaharie, D. Gorgan - Web and grid services for training in Earth observation, Proceedings of the 5th IEEE International Workshop on Intelligent Data Acquisition and Advanced ComputingSystems: Technology and Applications, IDAACS’2009 , art. no. 5342986, pp. 241-246, 2009.

19. Gog, C. Chira, D. Dumitrescu, D. Zaharie - Analysis of Some Mating and Collaboration Strategies in EvolutionaryAlgorithms, in V. Negru et al. (eds), Proceedings of SYNASC 2008, IEEE Computer Society, pp 538-542, 2009.

5 of 43

20. D. Zaharie - Statistical properties of differential evolution and related random search algorithms, in Paula Brito(ed.) Proceedings of International Conference on Computational Statistics Porto, Portugal, August 24-29, Physica-Verlag HD, ISBN978-3-7908-2083-6, pg. 473-485, 2008.

21. D. Zaharie, D. Petcu, S. Panica - A Hierarchical Approach in Distributed Evolutionary Algorithms for Multiob-jective Optimization , in I. Lirkov, S. Margenov, J. Wasniewski (eds) Proc. of LSSC 2007 - 6th InternationalConference on Large Scale Computing, LNCS 4818, ISBN-10 3-540-78825-5 Springer, pp. 505-514, 2008.

22. D. Zaharie, D. Lungeanu, F. Zamfirache - Interactive Search of Rules in Medical Data Using MultiobjectiveEvolutionary Algorithms , in Proceedings of the 2008 GECCO Conference Genetic and Evolutionary Computa-tion (workshop MedGEC–medical applications of genetic and evolutionary computation), Atlanta august 2008,ISBN:978-1-60558-131-6, pg. 2065-2072, 2008.

23. D. Lungeanu, D. Zaharie, F. Zamfirache - Influence of Missing Values Treatment on Classification Rules Evolvedfrom Medical Data. In I. Bichindaritz, P. Perner, L. G. Shapiro (Eds.): Advances in Data Mining. 8th IndustrialConference, ICDM 2008, Leipzig, Germany, July 2008, Poster and Workshop Proceedings. IbaI Publishing 2008,ISBN 978-3-940501-03-5, pg. 86-95, 2008.

24. S. Panica, D. Petcu, D. Zaharie - Evolutionary multi-objective optimization on Grid environments, in H. Burkhart(ed.), Procs. PDCN 2008, Parallel and Distributed Computing and Networks - 2008, Innsbruck, 11-14 feb., ActaPress, ISBN 978-0-88986-713-0, editor H. Burkhart, pg. 81-86, 2008.

25. R. Dogaru, D. Zaharie, D. Lungeanu, E. Bernad, M. Bari - A Framework for Mining Association Rules in Dataon Perinatal Care, Proceedings of CONTI 2008 (Conference on Technical Informatics, session on BiomedicalInformatics), Timisoara 4-6 iunie, vol 1, ISSN 1844-539X, pg. 147-152, 2008.

26. D. Zaharie, D. Lungeanu, S. Holban - Feature ranking based on weights estimated by multiobjective optimizationIn: J.Roth, J.Gutirrez, A.P. Abraham (eds.), Proceedings of IADIS First European Conference on Data Mining,5-7 iulie 2007, Lisabona, ISBN 978-972-8924-40-9, pg. 124-128, 2007.

27. D. Petcu, D. Zaharie, D. Gorgan et al.- MedioGrid: A grid-based platform for satellite image processing, in Proc.of 4th IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems, sep 06-08,2007 Dortmund, pg: 137-142, 2007.

28. D. Zaharie, S. Panica, M. Stoia-Djeska, M. Dragan, D. Petcu - Airfoil shape optimization by coupling computationalfluid dynamics with evolutionary multiobjective optimization, in M. Ganzha, M. Paprzycki, T. Pelech-Pilichowski(eds), Procs. Proceedings of the International Multiconference on Computer Science and Information Technology(IMCSIT 2007), October 15 17, 2007, Wisla, Poland, vol. 2, ISSN 1896-7094, pg. 323-325, 2007

29. D. Zaharie - A comparative analysis of crossover variants in differential evolution in M. Ganzha, M. Paprzycki, T.Pelech-Pilichowski (eds), Proc. of International Multiconference on Computer Science and Information TechnologyIMCSIT 2007, 15-17 oct., Wisla, Poland, ISSN 1896-7094, pg. 171-181, 2007

30. D. Petcu, D. Zaharie, H. Popa, M. Frincu, A. Eckstein - Grid Services Based on Heuristic Methods for Multi-Objective Optimization Problems, Procs. CSCS-16, 16th Intern. Conference on Control Systems and ComputerScience, Bucuresti, 22-25 May, 2007, EdituraPrintech, vol. 2, ISBN 978-973-718-743-7, pg. 136-141, 2007.

31. S. Panica, D. Petcu, D. Zaharie - A Grid-enabled Framework for Evolutionary Multiobjective Optimization,Procs. SACCS 2007, Iasi, 9th Internat. Symposium on Automatic Control and Computer Science, Proceedings,CD version, ISSN 1843-665-X, 2007.

32. D. Zaharie, D. Lungeanu, S. Holban, D. Navolan - Extracting prediction rules from medical data using evolutionaryalgorithms, Rev Med Chir Soc Med Nat Iasi (ISSN 0048-7848), Vol 111, Nr. 2 Supl.2: pg.197-202, 2007.

33. H. Popa, D.Pop, V. Negru, D. Zaharie - A Multi-Agent System for Knowledge Discovery from Databases, Proc.of SYNASC’07, IEEE Computer Society, ISBN 0-7695-3078-8, pg. 275-281, 2007.

34. D. Zaharie, S. Holban, D. Lungeanu, D. Navolan - A computational Intelligence Approach for Ranking Risk Factorsin Preterm Birth. In: Szakal A. (ed.). Proceedings of 4th International Symposium on Applied ComputationalIntelligence and Informatics - SACI2007, mai 2007, Timisoara, ISBN: 1-4244-1234-X, pg. 135-140, 2007.

35. D. Zaharie, F.Zamfirache, V.Negru, D.Pop, H. Popa - A Comparison of Quality Criteria for Unsupervised Cluster-ing of Documents Based on Differential Evolution, Proc. of International Conference on Knowledge Engineering:Principles and Techniques, Cluj-Napoca, 7-8 iunie, 2007, 114-121, 2007.

36. D. Zaharie, G. Ciobanu - Distributed Evolutionary Algorithms Inspired by Membranes in Continuous OptimizationProblems , in H.J. Hoogeboom, G. Paun, G. Rozenberg (eds), Pre-Proceedings of 7th Workshop on MembraneComputing, Leiden, 17-21 iulie, pp. 522-537, 2006.

37. D. Zaharie, D. Petcu - Communication Strategies in Distributed Evolutionary Algorithms for Multi-objectiveOptimization , Proc. of 7th International Conference on Technical Informatics, Timisoara, june 8-9, vol. 1, pp.145-150,2006.

38. D. Zaharie - Distributed Clustering Based on Representatives Evolved by Crowding Differential Evolution , in R.Matousek, P. Osmera (eds), Proc. of 12th International Conference on Soft Computing, Brno, may 31 - june 2,pp. 51-56, 2006.

39. D. Zaharie, F. Zamfirache - Diversity Enhancing Mechanisms for Evolutionary Optimization in Static and Dy-namic Environments , Proc. of 3rd Romanian-Hungarian Joint Symposium on Applied Computational Intelli-gence,Timisoara, may 25-26, pp. 460-471,2006.

6 of 43

40. D. Zaharie, F. Zamfirache - Dealing with noise in ant-based clustering , Proc. IEEE Congress of EvolutionaryComputation 2005, Edinburgh , 2-5 sept 2005, pg. 2395-2402.

41. D. Zaharie, F. Zamfirache - Ant-based clustering of medical data , HCMC 2005, First East European Conferenceon ”Health Care Modelling and Computation” (eds: F. Gorunescu, E. El-Darzi, M. Gorunescu), pg. 332-343,2005.

42. D. Zaharie - Density-based clustering with crowding differential evolution , Proc. 7 th Symposium on Symbolicand Numeric Algorithms for Scientific Computing, 25-29 sept. 2005, pg. 72-79, 2005.

43. I.Zaharie, D. Zaharie - Evolutionary optimization of molecular clusters, Proc. of 4th Conference on Isotopic andMolecular Processes, Cluj-Napoca, sept. 22-24, 2005 STUDIA UNIVERSITATIS BABES-BOLYAI, PHYSICA, L,4a, pg. 447-450, 2005.

44. D. Zaharie - Extensions of Differential Evolution Algorithms for Multimodal Optimization, in D. Petcu et.al (eds.),Proc. of SYNASC’04, 6th International Symposium of Symbolic and Numeric Algorithms for Scientific Computing,Timisoara, september 26-30, pp. 523-534, 2004.

45. D. Zaharie - A Multipopulation Differential Evolution Algorithm for Multimodal Optimization , in R. Matousek,P. Osmera (eds.), Proc. of Mendel 2004, 10th International Conference on Soft Computing, Brno, june 2004, pp.17-22, 2004.

46. C. Grosan, D. Zaharie - Base Changing Strategies in an Adaptive Representation Evolutionary Algorithm , Proc.of SACI’04 (1st Romanian-Hungarian Joint Symposium on Applied Computational Intelligence,Timisoara, may2004, pp. 79-88, 2004.

47. D. Zaharie, D. Petcu - Adaptive Pareto Differential Evolution and its Parallelization, Proc. of 5th InternationalConference on Parallel Processing and Applied Mathematics, Czestochowa, Poland, sept. 2003 , Lecture Notes inComputer Science Volume 3019, pp 261-268, 2004.

48. I.Zaharie, D. Zaharie -On the Determination of the Crystallization Energy in non-Isothermal Processes by usingEvolutionary Algorithms, 3rd Conference on Isotopic and Molecular Processes, Cluj-Napoca, sept. 2003, StudiaUniversitatis Babes-Bolyai, Physica special issue 2, XLVIII, pg. 317-320,2003.

49. D. Zaharie, D. Petcu - Parallel implementation of multi-population differential evolution In Proc. of 2nd Workshopon Concurrent Information Processing and Computing (CIPC’03), Sinaia, Romania, eds. D. Grigoras et al., 2003.

50. D. Zaharie - Control of Population Diversity and Adaptation in Differential Evolution Algorithms, in R. Matousek,P. Osmera (eds.), Proc. of Mendel 2003, 9th International Conference on Soft Computing, Brno, Czech Republic,june 2003, pp. 41-46, 2003.

51. D. Zaharie - Critical Values for the Control Parameters of Differential Evolution Algorithms, in R. Matousek, P.Osmera (eds.), Proc. of Mendel 2002, 8th International Conference on Soft Computing, Brno,Czech Republic, june2002, pp. 62-67, 2002.

52. D. Zaharie - On the Statistical Properties of Evolutionary Algorithms for Global Optimization, Proc. of the 9thSymposium of Mathematics and its Applications, ”Politehnica” Univ. Timisoara, nov. 2001.

53. D. Zaharie - Recombination Operators for Evolutionary Algorithms, Proc. of the XXVII Summer School of”Mathematics and its Applications in Engineering and Economics”, Sozopol, Bulgaria, june 2001.

54. D. Zaharie - Image Processing through Nonlinear Dynamical Systems, Proc. of the 8th Symposium of Mathematicsand its Applications, ”Politehnica” Univ. Timisoara, nov. 1999, pg. 413-420.

55. D. Zaharie - Nonlinear adaptive filters for image processing implemented through recurrent neural networks, Intern.Conference on Technical Informatics (Conti’98), Timisoara, 29-30 October, Bull. UPT, vol. 43(57), no.4, pp.12-21,1998.

56. D. Zaharie – Markovian modelling of stochastic neural networks with binary units, PAMM Conference (PC-122),Constanta, July 26- August 3, Bull. for Applied and Computer Mathematics, 1566-LXXXVI-B, pp.61-68, 1998.

57. D. Zaharie - Asymptotic behaviour of a class of nonlinear adaptive systems applied in image processing, PAMMConference (PC-122), Arad, July 22-25, Bull. for Applied and Computer Mathematics, 1566-LXXXVI-B, pp.505-514, 1998.

58. D. Zaharie - Networks of Binary Units with Stochastic Threshold, Proc. of the 6th Symposium of Mathematicsand its Applications, Timisoara, pp. 204-210, 1995.

59. D. Zaharie, I. Zaharie - Recurrent Neural Networks with Weighted Multiple-Time Step Parallel Dynamics, Proc.of International Conference on Technical Informatics, Timisoara, pp. 159-165, 1994.

60. D. Zaharie, I. Zaharie - Stability of Feedback Neural Networks with Stochastic Synaptic Weights, Proc. of the 6thSymposium of Mathematics and its Applications, Timisoara, pp. 310-317, 1993.

7 of 43

Appendix B. Citări1 (D. Zaharie)

Citări pentru D. Zaharie - D. Zaharie - Parameter Adaptation in Differential Evolution by Controlling thePopulation Diversity, Analele Univ. Timisoara (Proc. of SYNASC 2002), vol. XXXX, issue 2, 2002

1. Hu, Z., Su, Q., Yang, X., Xiong, Z.; Not guaranteeing convergence of differential evolution on a class of multimodalfunctions; 2016; Applied Soft Computing Journal; 41; 479; 487; 4; 10.1016/j.asoc.2016.01.001

2. Qiu, X., Xu, J.-X., Tan, K.C., Abbass, H.A.; Adaptive Cross-Generation Differential Evolution Operators forMultiobjective Optimization; 2016; IEEE Transactions on Evolutionary Computation; 20; 2; 7138611; 232; 244; 3;10.1109/TEVC.2015.2433672

3. Qiu, X., Xu, W., Tan, K.C., Xu, J.-X.; A new framework for self-adapting control parameters in multi-objectiveoptimization; 2015; GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference;743; 750; 10.1145/2739480.2754714

4. Thangavelu, S., Jeyakumar, G., Shunmuga Velyautham, C.; Population variance based empirical analysis ofthe behavior of differential evolution variants; 2015; Applied Mathematical Sciences; 9; 65-68; 3249; 3263; 1;10.12988/ams.2015.54312

5. Islam, S.M., Das, S., Ghosh, S., Roy, S., Suganthan, P.N.; An adaptive differential evolution algorithm with novelmutation and crossover strategies for global numerical optimization; 2012; IEEE Transactions on Systems, Man,and Cybernetics, Part B: Cybernetics; 42; 2; 6046144; 482; 500; 206; 10.1109/TSMCB.2011.2167966

6. Das, S., Suganthan, P.N.; Differential evolution: A survey of the state-of-the-art; 2011; IEEE Transactions onEvolutionary Computation; 15; 1; 5601760; 4; 31; 1552; 10.1109/TEVC.2010.2059031

7. Shan, D.M., Chen, Z.N., Wu, X.H.; Signal optimization for UWB radio systems; 2005; IEEE Transactions onAntennas and Propagation; 53; 7; 2178; 2184; 26; 10.1109/TAP.2005.850755

8. Jeyakumar, G., Velayutham, C.S.; Hybridizing differential evolution variants through heterogeneous mixing in adistributed framework; 2016; Studies in Computational Intelligence; 611; 107; 151; 10.1007/978-81-322-2544-7 4

9. Jeyakumar, G., Shunmuga Velayutham, C.; Hybridizing differential evolution variants through heterogeneousmixing in a distributed framework; 2015; Hybrid Soft Computing Approaches: Research and Applications; 611;107; 151; 2; 10.1007/978-81-322-2544-7 4

10. Reddy, R.R., Jeyakumar, G.; Differential evolution with added components for early detection and avoidanceof premature convergence in solving unconstrained global optimization problems; 2015; International Journal ofApplied Engineering Research; 10; 5; 13579; 13594;

11. Thangavelu, S., Jeyakumar, G., Balakrishnan, R.M., Velayutham, C.S.; Theoretical analysis of expected populationvariance evolution for a differential evolution variant; 2015; Smart Innovation, Systems and Technologies; 32; 403;416; 10.1007/978-81-322-2208-8 37

12. Basak, A., Maity, D., Das, S.; A differential invasive weed optimization algorithm for improved global numericaloptimization; 2013; Applied Mathematics and Computation; 219; 12; 6645; 6668; 18; 10.1016/j.amc.2012.12.057

13. Ghosh, S., Roy, S., Das, S., Abraham, A., Islam, S.M.; Peak-to-average power ratio reduction in OFDM systemsusing an adaptive differential evolution algorithm; 2011; 2011 IEEE Congress of Evolutionary Computation, CEC2011; 5949853; 1941; 1949; 3; 10.1109/CEC.2011.5949853

14. Basak, A., Pal, S., Abhyankar, A.R., Panigrahi, B.K.; Modified equivalent bilateral exchange of transmissionpricing using DIWO; 2010; 2010 Joint International Conference on Power Electronics, Drives and Energy Systems,PEDES 2010 and 2010 Power India; 5712557; ; 10.1109/PEDES.2010.5712557

15. Dasgupta, S., Das, S., Biswas, A., Abraham, A.; On stability and convergence of the population-dynamics indifferential evolution; 2009; AI Communications; 22; 1; 1; 20; 47; 10.3233/AIC-2009-0440

16. Das, S., Abraham, A., Konar, A.; Modeling and analysis of the population-dynamics of differential evolutionalgorithm; 2009; Studies in Computational Intelligence; 178; 111; 135; 10.1007/978-3-540-93964-1 3

17. Mukhopadhyay, A., Roy, A., Das, S., Das, S., Abraham, A.; Population-variance and explorative power of har-mony search: An analysis; 2008; 3rd International Conference on Digital Information Management, ICDIM 2008;4746793; 775; 781; 11; 10.1109/ICDIM.2008.4746793

1Sursa: SCOPUS - documente principale ş adiţionale. Lista include doar citări la lucrări citate de cel puţin 15 ori8 of 43

Citări pentru D. Zaharie - Critical Values for the Control Parameters of Differential Evolution Algorithms,in R. Matousek, P. Osmera (eds.), Proc. of Mendel 2002, 8th International Conference on Soft Computing,Brno,Czech Republic, june 2002, pp. 62-67, 2002.

1. Kukkonen, S., Coello Coello, C.A.; Generalized differential evolution for numerical and evolutionary optimization;2017; Studies in Computational Intelligence; 663; 253; 279; 10.1007/978-3-319-44003-3 11

2. Maciel, L., Gomide, F., Ballini, R.; A differential evolution algorithm for yield curve estimation; 2016; Mathematicsand Computers in Simulation; 129; 10; 30; 10.1016/j.matcom.2016.04.004

3. Campelo, F., Botelho, M.; Experimental investigation of recombination operators for differential evolution; 2016;GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference; 221; 228; 10.1145/2908812.2908852

4. Zheng, F., Zecchin, A.C., Maier, H.R., Simpson, A.R.; Comparison of the searching behavior of NSGA-II,SAMODE, and borg MOEAs applied to water distribution system design problems; 2016; Journal of Water Re-sources Planning and Management; 142; 7; 4016017; 10.1061/(ASCE)WR.1943-5452.0000650

5. Santhosh, E.C., Sangwai, J.S.; A hybrid differential evolution algorithm approach towards assisted history matchingand uncertainty quantification for reservoir models; 2016; Journal of Petroleum Science and Engineering; 142; 21;35; 10.1016/j.petrol.2016.01.038

6. Hu, Z., Su, Q., Yang, X., Xiong, Z.; Not guaranteeing convergence of differential evolution on a class of multimodalfunctions; 2016; Applied Soft Computing Journal; 41; 479; 487; 10.1016/j.asoc.2016.01.001

7. Dragoi, E.-N., Dafinescu, V.; Parameter control and hybridization techniques in differential evolution: a survey;2016; Artificial Intelligence Review; 45; 4; 447; 470; 10.1007/s10462-015-9452-8

8. Dragoi, E.N., Curteanu, S.; The use of differential evolution algorithm for solving chemical engineering problems;2016; Reviews in Chemical Engineering; 32; 2; 149; 180; 10.1515/revce-2015-0042

9. Cavalini, A.A., Lobato, F.S., Koroishi, E.H., Steffen, V.; Model updating of a rotating machine using the self-adaptive differential evolution algorithm; 2016; Inverse Problems in Science and Engineering; 24; 3; 504; 523;10.1080/17415977.2015.1047364

10. Lynn, N., Mallipeddi, R., Suganthan, P.N.; Self-adaptive ensemble differential evolution with sampled parametervalues for unit commitment; 2016; Lecture Notes in Computer Science (including subseries Lecture Notes inArtificial Intelligence and Lecture Notes in Bioinformatics); 9873 LNCS; 1; 16; 10.1007/978-3-319-48959-9 1

11. Raghu, R., Jeyakumar, G.; Mathematical modelling of migration process to measure population diversity of Dis-tributed Evolutionary Algorithms; 2016; Indian Journal of Science and Technology; 9; 31; 82410; 10.17485/ijst/2016/v9i31/82410

12. Kylheiko, K., Luukka, P., Jantunen, A., Heinrich, T.; How to win innovation races in high-tech industries? Anevolutionary optimisation model; 2016; International Journal of Technology Intelligence and Planning; 11; 1; 62;91; 10.1504/IJTIP.2016.074240

13. Moreno, L., Martin, F., Muoz, M.L., Garrido, S.; Differential Evolution Markov Chain Filter for Global Lo-calization; 2016; Journal of Intelligent and Robotic Systems: Theory and Applications; 82; 03.apr; 513; 536;10.1007/s10846-015-0245-8

14. Li, F.-W., Zhang, X.-Y., Zhu, J., Huang, Q.; Network security situation prediction based on APDE-RBF neuralnetwork; 2016; Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics; 38; 12; 2869; 2875;10.3969/j.issn.1001-506X.2016.12.28

15. Kukkonen, S., Coello, C.A.C.; Applying exponential weighting moving average control parameter adaptationtechnique with generalized differential evolution; 2016; 2016 IEEE Congress on Evolutionary Computation, CEC2016; 7744398; 4755; 4762; 10.1109/CEC.2016.7744398

16. Cihan, A., Birkholzer, J.T., Bianchi, M.; ”Optimal well placement and brine extraction for pressure managementduring CO< inf> 2< /inf> sequestration”; 2015; International Journal of Greenhouse Gas Control; 42;175; 187; 10.1016/j.ijggc.2015.07.025

17. Kushida, J.-I., Hara, A., Takahama, T.; Rank-based differential evolution with multiple mutation strategies forlarge scale global optimization; 2015; 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings;7256913; 353; 360; 10.1109/CEC.2015.7256913

18. Jin, C., Qin, A.K., Tang, K.; Local ensemble surrogate assisted crowding differential evolution; 2015; 2015 IEEECongress on Evolutionary Computation, CEC 2015 - Proceedings; 7256922; 433; 440; 10.1109/CEC.2015.7256922

19. Zheng, F., Zecchin, A.C., Simpson, A.R.; Investigating the run-time searching behavior of the differential evolutionalgorithm applied to water distribution system optimization; 2015; Environmental Modelling and Software; 69;292; 307; 10.1016/j.envsoft.2014.09.022

20. Mallipeddi, R., Lee, M.; An evolving surrogate model-based differential evolution algorithm; 2015; Applied SoftComputing Journal; 34; 770; 787; 10.1016/j.asoc.2015.06.010

21. Fan, Q., Yan, X.; Self-adaptive differential evolution algorithm with discrete mutation control parameters; 2015;Expert Systems with Applications; 42; 3; 1551; 1572; 10.1016/j.eswa.2014.09.046

22. Yang, M., Li, C., Cai, Z., Guan, J.; Differential evolution with auto-enhanced population diversity; 2015; IEEETransactions on Cybernetics; 45; 2; 6868218; 302; 315; 10.1109/TCYB.2014.2339495

9 of 43

23. Gil, D., Roche, D., Borris, A., Giraldo, J.; Terminating evolutionary algorithms at their steady state; 2015;Computational Optimization and Applications; 61; 2; 9722; 489; 515; 10.1007/s10589-014-9722-4


25. Locatelli, M., Vasile, M.; (Non) convergence results for the differential evolution method; 2015; OptimizationLetters; 9; 3; 413; 425; 10.1007/s11590-014-0816-9

26. Raghu, R., Jeyakumar, G.; Empirical analysis on the population diversity of the sub-population in distributeddifferential evolution algorithm; 2015; International Journal of Control Theory and Applications; 8; 5; 1809; 1816;

27. Thangavelu, S., Jeyakumar, G., Shunmuga Velyautham, C.; Population variance based empirical analysis of the be-havior of differential evolution variants; 2015; Applied Mathematical Sciences; 9; 65-68; 3249; 3263; 10.12988/ams.2015.54312

28. Drozdik, M., Aguirre, H., Akimoto, Y., Tanaka, K.; Comparison of parameter control mechanisms in multi-objective differential evolution; 2015; Lecture Notes in Computer Science (including subseries Lecture Notes inArtificial Intelligence and Lecture Notes in Bioinformatics); 8994; 89; 103; 10.1007/978-3-319-19084-6 8

29. Miruna Joe Amali, S., Baskar, S.; Surrogate assisted-hybrid differential evolution algorithm using diversity control;2015; Expert Systems; 32; 4; 531; 545; 10.1111/exsy.12105

30. Al-Dabbagh, R.D., Botzheim, J., Al-Dabbagh, M.D.; Comparative analysis of a modified differential evolutionalgorithm based on bacterial mutation scheme; 2015; IEEE SSCI 2014 - 2014 IEEE Symposium Series on Com-putational Intelligence - SDE 2014: 2014 IEEE Symposium on Differential Evolution, Proceedings; 7031532;10.1109/SDE.2014.7031532

31. Martn, F., Monje, C.A., Moreno, L., Balaguer, C.; DE-based tuning of PI?D? controllers; 2015; ISA Transactions;59; 398; 407; 10.1016/j.isatra.2015.10.002

32. Zheng, F.; Comparing the real-time searching behavior of four differential-evolution variants applied to water-distribution-network design optimization; 2015; Journal of Water Resources Planning and Management; 141; 10;4015016; 10.1061/(ASCE)WR.1943-5452.0000534

33. Al-Nemer, A.A., Issaka, M.B., Awotunde, A.A., Al-Hashem, H.S.; Global optimization strategies for well testsin dual porosity reservoirs; 2015; SPE Middle East Oil and Gas Show and Conference, MEOS, Proceedings;2015-January; 716; 739;

34. P. Silva, R.C., Lopes, R.A., R. Freitas, A.R., Guimar?es, F.G.; A study on self-configuration in the differentialevolution algorithm; 2015; IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - SDE2014: 2014 IEEE Symposium on Differential Evolution, Proceedings; 7031531; 10.1109/SDE.2014.7031531

35. Al-Dabbagh, R.D., Mekhilef, S., Baba, M.S.; Parameters’ fine tuning of differential evolution algorithm; 2015;Computer Systems Science and Engineering; 30; 2; 125; 139;

36. Maier, H.R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L.S., Cunha, M.C., Dandy, G.C., Gibbs, M.S., Keedwell,E., Marchi, A., Ostfeld, A., Savic, D., Solomatine, D.P., Vrugt, J.A., Zecchin, A.C., Minsker, B.S., Barbour, E.J.,Kuczera, G., Pasha, F., Castelletti, A., Giuliani, M., Reed, P.M.; Evolutionary algorithms and other metaheuristicsin water resources: Current status, research challenges and future directions; 2014; Environmental Modelling andSoftware; 62; 271; 299; 10.1016/j.envsoft.2014.09.013

37. Coelho, L.D.S., Ayala, H.V.H., Mariani, V.C.; A self-adaptive chaotic differential evolution algorithm using gammadistribution for unconstrained global optimization; 2014; Applied Mathematics and Computation; 234; 452; 459;10.1016/j.amc.2014.01.159

38. Raghunathan, T., Ghose, D.; Differential evolution based 3-D guidance law for a realistic interceptor model; 2014;Applied Soft Computing Journal; 16; 20; 33; 10.1016/j.asoc.2013.11.017

39. Drozdik, M., Tanaka, K., Aguirre, H., Verel, S., Liefooghe, A., Derbel, A.B.; An analysis of differential evolutionparameters on rotated bi-objective optimization functions; 2014; Lecture Notes in Computer Science (includingsubseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 8886; 143; 154;

40. Qin, A.K., Tang, K., Pan, H., Xia, S.; Self-adaptive differential evolution with local search chains for real-parametersingle-objective optimization; 2014; Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC2014; 6900636; 467; 474; 10.1109/CEC.2014.6900636

41. Dai, Q.-W., Jiang, F.-B., Dong, L.; Nonlinear inversion for electrical resistivity tomography based on chaoticDE-BP algorithm; 2014; Journal of Central South University; 21; 5; 2018; 2025; 10.1007/s11771-014-2151-9

42. Segura, C., Coello, C.A.C., Segredo, E., Len, C.; An analysis of the automatic adaptation of the crossover ratein differential evolution; 2014; Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014;6900585; 459; 466; 10.1109/CEC.2014.6900585

43. Aalto, J., Lampinen, J.; A mutation and crossover adaptation mechanism for differential evolution algorithm;2014; Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014; 6900532; 451; 458;10.1109/CEC.2014.6900532

44. Kazimipour, B., Li, X., Qin, A.K.; Effects of population initialization on differential evolution for large scaleoptimization; 2014; Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014; 6900624;2404; 2411; 10.1109/CEC.2014.6900624

10 of 43

45. Subramanian, S.V., Delaurentis, D.A.; A hybrid differential evolution self-organizing-map algorithm for optimiza-tion of expensive black-box functions; 2014; AIAA AVIATION 2014 -15th AIAA/ISSMO Multidisciplinary Analysisand Optimization Conference; ;

46. Locatelli, M., Maischberger, M., Schoen, F.; Differential evolution methods based on local searches; 2014; Com-puters and Operations Research; 43; 1; 169; 180; 10.1016/j.cor.2013.09.010

47. Das, S., Mandal, A., Mukherjee, R.; An adaptive differential evolution algorithm for global optimization in dynamicenvironments; 2014; IEEE Transactions on Cybernetics; 44; 6; 6587535; 966; 978; 10.1109/TCYB.2013.2278188

48. Roche, D., Gil, D., Giraldo, J.; Detecting loss of diversity for an efficient termination of EAs; 2014; Proceedings- 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2013;6821196; 561; 566; 10.1109/SYNASC.2013.79

49. Chiu, Y.-C.; Application of differential evolutionary optimization methodology for parameter structure identifica-tion in groundwater modeling; 2014; Hydrogeology Journal; 22; 8; 1731; 1748; 10.1007/s10040-014-1172-7

50. Yang, K., Mu, L., Yang, D., Zou, F., Wang, L., Jiang, Q.; Multiobjective memetic estimation of distributionalgorithm based on an incremental tournament local searcher; 2014; Scientific World Journal; 2014; 836272;10.1155/2014/836272

51. Peng, H., Wu, Z.-J., Zhou, X.-Y., Deng, C.-S.; Dynamic differential evolution algorithm based on elite local learn-ing; 2014; Tien Tzu Hsueh Pao/Acta Electronica Sinica; 42; 8; 1522; 1530; 10.3969/j.issn.0372-2112.2014.08.010

52. Kushida, J.-I., Hara, A., Takahama, T., Kido, A.; Island-based differential evolution with varying subpopulationsize; 2013; 2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013- Proceedings; 6624798; 119; 124; 10.1109/IWCIA.2013.6624798

53. Juszczuk, P., Boryczka, U.; The differential evolution with the entropy based population size adjustment for thenash equilibria problem; 2013; Lecture Notes in Computer Science (including subseries Lecture Notes in ArtificialIntelligence and Lecture Notes in Bioinformatics); 8083 LNAI; 691; 700; 10.1007/978-3-642-40495-5 69

54. Zhou, Y., Li, X., Gao, L.; A novel two-layer hierarchical differential evolution algorithm for global optimization;2013; Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013; 6722250;2916; 2921; 10.1109/SMC.2013.497

55. Sarkar, S., Patra, G.R., Das, S., Chaudhuri, S.S.; Fuzzy clustering of image pixels with a fitness-based adaptivedifferential evolution; 2013; Lecture Notes in Computer Science (including subseries Lecture Notes in ArtificialIntelligence and Lecture Notes in Bioinformatics); 8297 LNCS; PART 1; 179; 188; 10.1007/978-3-319-03753-0 17

56. Escalona-Vargas, D.I., Gutirrez, D., Lopez-Arevalo, I.; Performance of different metaheuristics in EEG source local-ization compared to the Cramr-Rao bound; 2013; Neurocomputing; 120; 597; 609; 10.1016/j.neucom.2013.04.010

57. Hu, Z., Xiong, S., Su, Q., Zhang, X.; Sufficient conditions for global convergence of differential evolution algorithm;2013; Journal of Applied Mathematics; 2013; 193196; 10.1155/2013/193196

58. Opara, K., Arabas, J.; Censoring mutation in differential evolution; 2013; Proceedings of the 2013 IEEE Symposiumon Differential Evolution, SDE 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013;6601442; 54; 60; 10.1109/SDE.2013.6601442

59. Li, D., Chen, J., Xin, B.; A novel Differential Evolution algorithm with Gaussian mutation that balances explo-ration and exploitation; 2013; Proceedings of the 2013 IEEE Symposium on Differential Evolution, SDE 2013 - 2013IEEE Symposium Series on Computational Intelligence, SSCI 2013; 6601437; 18; 24; 10.1109/SDE.2013.6601437

60. Deng, W., Yang, X., Zou, L., Wang, M., Liu, Y., Li, Y.; An improved self-adaptive differential evolution algorithmand its application; 2013; Chemometrics and Intelligent Laboratory Systems; 128; 66; 76; 10.1016/j.chemolab.2013.07.004

61. Mallipeddi, R.; Harmony search based parameter ensemble adaptation for differential evolution; 2013; Journal ofApplied Mathematics; 2013; 750819; 10.1155/2013/750819

62. Olguin-Carbajal, M., Alba, E., Arellano-Verdejo, J.; Micro-differential evolution with local search for high di-mensional problems; 2013; 2013 IEEE Congress on Evolutionary Computation, CEC 2013; 6557552; 48; 54;10.1109/CEC.2013.6557552

63. Qin, A.K., Li, X.; Differential evolution on the CEC-2013 single-objective continuous optimization testbed; 2013;2013 IEEE Congress on Evolutionary Computation, CEC 2013; 6557689; 1099; 1106; 10.1109/CEC.2013.6557689

64. Qin, A.K., Li, X., Pan, H., Xia, S.; Investigation of self-adaptive differential evolution on the CEC-2013 real-parameter single-objective optimization testbed; 2013; 2013 IEEE Congress on Evolutionary Computation, CEC2013; 6557690; 1107; 1114; 10.1109/CEC.2013.6557690

65. Bolufe-Rohler, A., Estevez-Velarde, S., Piad-Morffis, A., Chen, S., Montgomery, J.; Differential evolution withthresheld convergence; 2013; 2013 IEEE Congress on Evolutionary Computation, CEC 2013; 6557551; 40; 47;10.1109/CEC.2013.6557551

66. Aalto, J., Lampinen, J.; A mutation adaptation mechanism for Differential Evolution algorithm; 2013; 2013 IEEECongress on Evolutionary Computation, CEC 2013; 6557553; 55; 62; 10.1109/CEC.2013.6557553

67. Coelho, L.D.S., Mariani, V.C., Ferreira Da Luz, M.V., Leite, J.V.; Novel gamma differential evolution approachfor multiobjective transformer design optimization; 2013; IEEE Transactions on Magnetics; 49; 5; 6514762; 2121;2124; 10.1109/TMAG.2013.2243134 11 of 43

68. Wang, Z., Zhang, W.; Spectrum sharing with limited channel feedback; 2013; IEEE Transactions on WirelessCommunications; 12; 5; 6493533; 2524; 2532; 10.1109/TWC.2013.032513.121510

69. Brest, J., Korovec, P., Pilc, J., Zamuda, A., Boskovic, B., Maucec, M.S.; Differential evolution and differentialant-stigmergy on dynamic optimisation problems; 2013; International Journal of Systems Science; 44; 4; 663; 679;10.1080/00207721.2011.617899

70. Poursalehi, N., Zolfaghari, A., Minuchehr, A.; Differential harmony search algorithm to optimize PWRs loadingpattern; 2013; Nuclear Engineering and Design; 257; 161; 174; 10.1016/j.nucengdes.2013.01.020

71. Mandal, K.K., Chakraborty, N.; Parameter study of differential evolution based optimal scheduling of hydrothermalsystems; 2013; Journal of Hydro-Environment Research; 7; 1; 72; 80; 10.1016/j.jher.2012.04.001

72. Weber, M., Neri, F., Tirronen, V.; A study on scale factor/crossover interaction in distributed differential evolution;2013; Artificial Intelligence Review; 39; 3; 195; 224; 10.1007/s10462-011-9267-1

73. Alam Anik, M.T., Md Noman, A.S., Ahmed, S.; Preserving rotation invariant properties in differential evolutionalgorithm; 2013; Proceedings of 2013 2nd International Conference on Advances in Electrical Engineering, ICAEE2013; 6750339; 235; 240; 10.1109/ICAEE.2013.6750339

74. Aalto, J., Lampinen, J.; A crossover adaptation mechanism for differential evolution algorithm; 2013; Mendel; 19;24;

75. Zhou, Y., Li, X., Gao, L.; A differential evolution algorithm with intersect mutation operator; 2013; Applied SoftComputing Journal; 13; 1; 390; 401; 10.1016/j.asoc.2012.08.014

76. Zhu, J., Yan, X.; Adaptive variable space differential evolution algorithm based on population distribution; 2013;Memetic Computing; 5; 1; 49; 64; 10.1007/s12293-012-0103-1

77. Falco, I.D.; Differential Evolution for automatic rule extraction from medical databases; 2013; Applied Soft Com-puting Journal; 13; 2; 1265; 1283; 10.1016/j.asoc.2012.10.022

78. Dragoi, E.-N., Curteanu, S., Galaction, A.-I., Cascaval, D.; Optimization methodology based on neural networksand self-adaptive differential evolution algorithm applied to an aerobic fermentation process; 2013; Applied SoftComputing Journal; 13; 1; 222; 238; 10.1016/j.asoc.2012.08.004

79. Sai Hanuman, A., Anand, S., Vinaya Babu, A., Govardhan, A.; Application of dynamic clustering using ADE totransportation planning; 2012; Advances in Intelligent and Soft Computing; 132 AISC; 669; 678; 10.1007/978-3-642-27443-5-77

80. Maciel, L., Gomide, F., Ballini, R.; Modeling the term structure of government bond yields with a differentialevolution algorithm; 2012; 2012 IEEE Conference on Computational Intelligence for Financial Engineering andEconomics, CIFEr 2012 - Proceedings; 6327794; 212; 219; 10.1109/CIFEr.2012.6327794

81. Ahmadi-Nedushan, B.; Prediction of elastic modulus of normal and high strength concrete using ANFIS and opti-mal nonlinear regression models; 2012; Construction and Building Materials; 36; 665; 673; 10.1016/j.conbuildmat.2012.06.002

82. Sorsa, A., Koskenniemi, A., Leivisk, K.; Differential evolution in parameter identification fuel cell as an example;2012; ICINCO 2012 - Proceedings of the 9th International Conference on Informatics in Control, Automation andRobotics; 1; 40; 49;

83. Montgomery, J., Chen, S.; A simple strategy for maintaining diversity and reducing crowding in differential evolu-tion; 2012; 2012 IEEE Congress on Evolutionary Computation, CEC 2012; 6252891; 10.1109/CEC.2012.6252891

84. Ahmadi-Nedushan, B.; An optimized instance based learning algorithm for estimation of compressive strength ofconcrete; 2012; Engineering Applications of Artificial Intelligence; 25; 5; 1073; 1081; 10.1016/j.engappai.2012.01.012

85. Bi, X., Liu, G., Xiao, J.; Dynamic adaptive differential evolution based on novel mutation strategy; 2012; JisuanjiYanjiu yu Fazhan/Computer Research and Development; 49; 6; 1288; 1297;

86. Jia, L., Zhang, C.; An improved self-adaptive control parameter of differential evolution for global optimiza-tion; 2012; International Journal of Digital Content Technology and its Applications; 6; 8; 343; 350; 10.4156/jd-cta.vol6.issue8.40

87. Zhabitskaya, E.; Constraints on control parameters of asynchronous differential evolution; 2012; Lecture Notes inComputer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);7125 LNCS; 322; 327; 10.1007/978-3-642-28212-6 40

88. Kushida, J., Oba, K., Kamei, K.; An analysis of behavior of differential evolution based on characteristics inheri-tance; 2012; ICIC Express Letters; 6; 3; 581; 588;

89. Wang, S., Ding, L., Shu, W., Xie, C., Yang, H.; Differential evolution without scaling factor; 2012; InternationalJournal of Advancements in Computing Technology; 4; 4; 41; 48; 10.4156/ijact.vol4.issue4.6

90. Peng, L., Wang, Y., Dai, G., Cao, Z.; A novel differential evolution with uniform design for continuous globaloptimization; 2012; Journal of Computers; 7; 1; 3; 10; 10.4304/jcp.7.1.3-10

91. Ghosh, S., Das, S., Vasilakos, A.V., Suresh, K.; On convergence of differential evolution over a class of continuousfunctions with unique global optimum; 2012; IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics; 42; 1; 5961644; 107; 124; 10.1109/TSMCB.2011.2160625

92. Spadoni, M., Stefanini, L.; A Differential Evolution algorithm to deal with box, linear and quadratic-convexconstraints for boundary optimization; 2012; Journal of Global Optimization; 52; 1; 171; 192; 10.1007/s10898-011-9695-0

12 of 43

93. Yang, Z., Li, X., Bowers, C.P., Schnier, T., Tang, K., Yao, X.; An efficient evolutionary approach to parameteridentification in a building thermal model; 2012; IEEE Transactions on Systems, Man and Cybernetics Part C:Applications and Reviews; 42; 6; 6105579; 957; 969; 10.1109/TSMCC.2011.2174983

94. Pankratius, V., Blaese, S.; Application-level automatic performance tuning on the single-chip cloud computer;2011; 3rd Many-Core Applications Research Community Symposium, MARC 2011; 1; 6;

95. Tripathi, S.S., Panda, S.; Tuning of power system stabilizer employing differential evolution optimization algorithm;2011; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and LectureNotes in Bioinformatics); 7076 LNCS; PART 1; 59; 67; 10.1007/978-3-642-27172-4 8

96. Jeyakumar, G., Shanmugavelayutham, C.; Empirical measurements on the convergence nature of differential evo-lution variants; 2011; Communications in Computer and Information Science; 131 CCIS; PART 1; 472; 480;10.1007/978-3-642-17857-3 46

97. Wan, S., Xiong, S., Kou, J.; An improved trigonometric differential evolution; 2011; International Journal ofAdvancements in Computing Technology; 3; 11; 156; 162; 10.4156/ijact.vol3.issue11.20

98. Sharma, T.K., Pant, M.; Self adaptive mutation step size in differential evolution algorithm; 2011; Proceedingsof the 2011 World Congress on Information and Communication Technologies, WICT 2011; 6141238; 171; 175;10.1109/WICT.2011.6141238

99. Pedersen, M.E.H., Chipperfield, A.J.; Tuning differential evolution for artificial neural networks; 2011; New De-velopments in Artificial Neural Networks Research; 277; 294;

100. Alguliev, R.M., Aliguliyev, R.M., Mehdiyev, C.A.; Sentence selection for generic document summarization us-ing an adaptive differential evolution algorithm; 2011; Swarm and Evolutionary Computation; 1; 4; 213; 222;10.1016/j.swevo.2011.06.006

101. Yang, Z., Tang, K., Yao, X.; Scalability of generalized adaptive differential evolution for large-scale continuousoptimization; 2011; Soft Computing; 15; 11; 2141; 2155; 10.1007/s00500-010-0643-6

102. Brest, J., Maucec, M.S.; Self-adaptive differential evolution algorithm using population size reduction and threestrategies; 2011; Soft Computing; 15; 11; 2157; 2174; 10.1007/s00500-010-0644-5

103. Zhang, X., Jiang, X., Scott, P.J.; A minimax fitting algorithm for ultra-precision aspheric surfaces; 2011; Journalof Physics: Conference Series; 311; 1; 12031; 10.1088/1742-6596/311/1/012031

104. Zhang, X., Liu, S.; Almost-parameter-free differential evolution; 2011; Proceedings - 2011 7th International Con-ference on Natural Computation, ICNC 2011; 3; 6022358; 1461; 1465; 10.1109/ICNC.2011.6022358

105. Dragoi, E.-N., Curteanu, S., Leon, F., Galaction, A.-I., Cascaval, D.; Modeling of oxygen mass transfer in thepresence of oxygen-vectors using neural networks developed by differential evolution algorithm; 2011; EngineeringApplications of Artificial Intelligence; 24; 7; 1214; 1226; 10.1016/j.engappai.2011.06.004

106. Adi, V.S.K., Chang, C.-T.; Two-tier search strategy to identify nominal operating conditions for maximum flexi-bility; 2011; Industrial and Engineering Chemistry Research; 50; 18; 10707; 10716; 10.1021/ie201050w

107. Gang, H., Shaolei, L., Jinhang, L., Bo, F.; An improved differential evolution algorithm for mixed-model as-sembly sequencing; 2011; Communications in Computer and Information Science; 227 CCIS; PART 4; 588; 597;10.1007/978-3-642-23226-8 76

108. Moreno, L., Blanco, D., Muoz, M.L., Garrido, S.; L1L2-norm comparison in global localization of mobile robots;2011; Robotics and Autonomous Systems; 59; 9; 597; 610; 10.1016/j.robot.2011.04.006

109. Shi, E.C., Leung, F.H.F., Lai, J.C.Y.; An adaptive differential evolution with unsymmetrical mutation; 2011; 2011IEEE Congress of Evolutionary Computation, CEC 2011; 5949844; 1879; 1886; 10.1109/CEC.2011.5949844

110. Noman, N., Bollegala, D., Iba, H.; An adaptive differential evolution algorithm; 2011; 2011 IEEE Congress ofEvolutionary Computation, CEC 2011; 5949891; 2229; 2236; 10.1109/CEC.2011.5949891

111. Montgomery, J., Randall, M., Lewis, A.; Differential evolution for RFID antenna design: A comparison with antcolony optimisation; 2011; Genetic and Evolutionary Computation Conference, GECCO’11; 673; 680; 10.1145/2001576.2001669

112. Polakova, R., Tvrdik, J.; Various mutation strategies in enhanced competitive differential evolution for constrainedoptimization; 2011; IEEE SSCI 2011 - Symposium Series on Computational Intelligence - SDE 2011: 2011 IEEESymposium on Differential Evolution; 5952070; 17; 24; 10.1109/SDE.2011.5952070

113. Weber, M., Neri, F., Tirronen, V.; A study on scale factor in distributed differential evolution; 2011; InformationSciences; 181; 12; 2488; 2511; 10.1016/j.ins.2011.02.008

114. Wan, S., Xiong, S., Kou, J., Liu, Y.; Differential evolution with improved mutation strategy; 2011; Lecture Notes inComputer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);6728 LNCS; PART 1; 431; 438; 10.1007/978-3-642-21515-5 51

115. Panda, S.; Robust coordinated design of multiple and multi-type damping controller using differential evolution al-gorithm; 2011; International Journal of Electrical Power and Energy Systems; 33; 4; 1018; 1030; 10.1016/j.ijepes.2011.01.019

116. Vasile, M., Minisci, E., Locatelli, M.; An inflationary differential evolution algorithm for space trajectory optimiza-tion; 2011; IEEE Transactions on Evolutionary Computation; 15; 2; 5688231; 267; 281; 10.1109/TEVC.2010.2087026

117. Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.; Differential evolution algorithm with ensemble of pa-rameters and mutation strategies; 2011; Applied Soft Computing Journal; 11; 2; 1679; 1696; 10.1016/j.asoc.2010.04.024

13 of 43

118. Yamaguchi, S.; An automatic control parameter tuning method for differential evolution; 2011; Electrical Engi-neering in Japan (English translation of Denki Gakkai Ronbunshi); 174; 3; 25; 33; 10.1002/eej.21047

119. Das, S., Suganthan, P.N.; Differential evolution: A survey of the state-of-the-art; 2011; IEEE Transactions onEvolutionary Computation; 15; 1; 5601760; 4; 31; 10.1109/TEVC.2010.2059031

120. Liu, Y., Li, S.; A new differential evolutionary algorithm with neighborhood search; 2011; Information TechnologyJournal; 10; 3; 573; 578; 10.3923/itj.2011.573.578

121. Nakawiro, W., Erlich, I.; A new adaptive differential evolution algorithm for voltage stability constrained optimalpower flow; 2011; 17th Power Systems Computation Conference, PSCC 2011; ;

122. Rocca, P., Oliveri, G., Massa, A.; Differential evolution as applied to electromagnetics; 2011; IEEE Antennas andPropagation Magazine; 53; 1; 38; 49;

123. Iba, H., Noman, N.; New frontier in evolutionary algorithms: Theory and applications; 2011; New Frontier inEvolutionary Algorithms: Theory and Applications; 1; 304; 10.1142/P769

124. Urfalioglu, O., Arikan, O.; Self-adaptive randomized and rank-based differential evolution for multimodal problems;2011; Journal of Global Optimization; 51; 4; 607; 640; 10.1007/s10898-011-9646-9

125. Montgomery, J.; Crossover and the different faces of differential evolution searches; 2010; 2010 IEEE WorldCongress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC2010; 5586184; 10.1109/CEC.2010.5586184

126. Jeyakumar, G., Shanmugavelayutham, C.; Analyzing the explorative power of differential evolution variants ondifferent classes of problems; 2010; Lecture Notes in Computer Science (including subseries Lecture Notes inArtificial Intelligence and Lecture Notes in Bioinformatics); 6466 LNCS; 95; 102; 10.1007/978-3-642-17563-3 12

127. Liu, Y., Li, S.; Differential evolution with neighborhood search; 2010; 2010 2nd International Conference onComputational Intelligence and Natural Computing, CINC 2010; 1; 5643890; 76; 79; 10.1109/CINC.2010.5643890

128. Panda, S., Swain, S.C., Baliarsingh, A.K.; Differential evolution algorithm for simultaneous tuning of excita-tion and FACTS-based controller; 2010; International Journal of Bio-Inspired Computation; 2; 6; 404; 412;10.1504/IJBIC.2010.037020

129. Montgomery, J., Chen, S.; An analysis of the operation of differential evolution at high and low crossover rates;2010; 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolution-ary Computation, CEC 2010; 5586128; 10.1109/CEC.2010.5586128

130. Sanko, J., Penjam, J.; Differential evolutionary approach guided by the Functional Constraint Network to solveprogram synthesis problem; 2010; 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010IEEE Congress on Evolutionary Computation, CEC 2010; 5586092; 10.1109/CEC.2010.5586092

131. Tvrdik, J., Polakova, R.; Competitive differential evolution for constrained problems; 2010; 2010 IEEE WorldCongress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC2010; 5586299; 10.1109/CEC.2010.5586299

132. Peng, L., Dai, G., Wang, Y.; UDE: Differential evolution with uniform design; 2010; Proceedings - 3rd Inter-national Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2010; 5715089; 239; 246;10.1109/PAAP.2010.61

133. Peng, L., Wang, Y.; Differential evolution using uniform-quasi-opposition for initializing the population; 2010;Information Technology Journal; 9; 8; 1629; 1634;

134. Tvrdik, J., Polakova, R.; Enhanced competitive differential evolution for constrained optimization; 2010; Proceed-ings of the International Multiconference on Computer Science and Information Technology, IMCSIT 2010; 5;5680058; 909; 915;

135. Roy, G.G., Chakraborty, P., Das, S.; Designing fractional-order PI?D? controller using differential harmony searchalgorithm; 2010; International Journal of Bio-Inspired Computation; 2; 5; 303; 309; 10.1504/IJBIC.2010.036156

136. Lobato, F.S., Steffen Jr., V., Neto, A.J.S.; Self-adaptive differential evolution based on the concept of populationdiversity applied to simultaneous estimation of anisotropic scattering phase function, albedo and optical thickness;2010; CMES - Computer Modeling in Engineering and Sciences; 69; 1; 1; 17;

137. Neri, F., Tirronen, V.; Recent advances in differential evolution: A survey and experimental analysis; 2010;Artificial Intelligence Review; 33; 01.feb; 61; 106; 10.1007/s10462-009-9137-2

138. Rocca, P., Benedetti, M., Donelli, M., Franceschini, D., Massa, A.; Evolutionary optimization as applied to inversescattering problems; 2009; Inverse Problems; 25; 12; 123003; 10.1088/0266-5611/25/12/123003

139. Montgomery, J.; The effects of different kinds of move in differential evolution searches; 2009; Lecture Notes inComputer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics);5865 LNAI; 272; 281; 10.1007/978-3-642-10427-5 27

140. Jia, L., Gong, W., Wu, H.; An improved self-adaptive control parameter of differential evolution for global optimiza-tion; 2009; Communications in Computer and Information Science; 51; 215; 224; 10.1007/978-3-642-04962-0 25

141. Cervenka, M., Boudn, H.; Visual aid for better control parameter configuration of differential evolution; 2009;PACIIA 2009 - 2009 2nd Asia-Pacific Conference on Computational Intelligence and Industrial Applications; 1;5406393; 439; 442; 10.1109/PACIIA.2009.5406393

14 of 43

142. Chakraborty, P., Roy, G.G., Das, S., Jain, D., Abraham, A.; An improved harmony search algorithm with differ-ential mutation operator; 2009; Fundamenta Informaticae; 95; 4; 401; 426; 10.3233/FI-2009-157

143. Montgomery, J.; Differential evolution: Difference vectors and movement in solution space; 2009; 2009 IEEECongress on Evolutionary Computation, CEC 2009; 4983298; 2833; 2840; 10.1109/CEC.2009.4983298

144. Brest, J., Zamuda, A., Boskovic, B., Maccec, M.S., Zumer, V.; Dynamic optimization using self-adaptive dif-ferential evolution; 2009; 2009 IEEE Congress on Evolutionary Computation, CEC 2009; 4982976; 415; 422;10.1109/CEC.2009.4982976

145. Kukkonen, S., Lampinen, J.; Performance assessment of generalized differential evolution 3 with a Given set ofconstrained multi-objective test problems; 2009; 2009 IEEE Congress on Evolutionary Computation, CEC 2009;4983178; 1943; 1950; 10.1109/CEC.2009.4983178

146. Jeyakumar, G., Shanmugavelayutham, C.; An empirical comparison of differential evolution variants for highdimensional function optimization; 2009; 2009 International Conference on Intelligent Agent and Multi-AgentSystems, IAMA 2009; 5228018; 10.1109/IAMA.2009.5228018

147. Panda, S.; Differential evolutionary algorithm for TCSC-based controller design; 2009; Simulation ModellingPractice and Theory; 17; 10; 1618; 1634; 10.1016/j.simpat.2009.07.002

148. Qing, A.; Differential Evolution: Fundamentals and Applications in Electrical Engineering; 2009; DifferentialEvolution: Fundamentals and Applications in Electrical Engineering; 1; 418; 10.1002/9780470823941

149. Reynoso-Meza, G., Sanchis, J., Blasco, X.; An adaptive parameter control for the differential evolution algorithm;2009; Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and LectureNotes in Bioinformatics); 5517 LNCS; PART 1; 375; 382; 10.1007/978-3-642-02478-8 47

150. Neri, F., Tirronen, V.; Memetic differential evolution frameworks in filter design for defect detection in paperproduction; 2009; Studies in Computational Intelligence; 213; 113; 131; 10.1007/978-3-642-01636-3 7

151. Zhang, X.-W., Liu, S.-Y.; Differential evolution without the scale factor F; 2009; Tien Tzu Hsueh Pao/ActaElectronica Sinica; 37; 6; 1318; 1323;

152. Tvrdik, J.; Adaptation in differential evolution: A numerical comparison; 2009; Applied Soft Computing Journal;9; 3; 1149; 1155; 10.1016/j.asoc.2009.02.010

153. Das, S., Abraham, A., Konar, A.; Differential evolution algorithm: Foundations and perspectives; 2009; Studiesin Computational Intelligence; 178; 63; 110; 10.1007/978-3-540-93964-1 2

154. Panda, S., Sahu, R.K.; Modelling, simulation and optimal design of a TCSC-based controller in a power system;2009; International Journal of Engineering Systems Modelling and Simulation; 1; 4; 222; 230; 10.1504/IJESMS.2009.031355

155. Caponio, A., Neri, F., Tirronen, V.; Super-fit control adaptation in memetic differential evolution frameworks;2009; Soft Computing; 13; 08.sep; 811; 831; 10.1007/s00500-008-0357-1

156. Yamaguchi, S.; An automatic control parameters tuning method for differential evolution; 2008; IEEJ Transactionson Electronics, Information and Systems; 128; 11; 10.1541/ieejeiss.128.1696

157. Tvrdik, J.; Adaptive differential evolution and exponential crossover; 2008; Proceedings of the International Mul-ticonference on Computer Science and Information Technology, IMCSIT 2008; 3; 4747353; 927; 931; 10.1109/IM-CSIT.2008.4747353

158. Kukkonen, S., Lampinen, J.; Generalized differential evolution for constrained multi-objective optimization; 2008;Multi-Objective Optimization in Computational Intelligence: Theory and Practice; 43; 75; 10.4018/978-1-59904-498-9.ch003

159. Yang, Z., Tang, K., Yao, X.; Self-adaptive differential evolution with neighborhood search; 2008; 2008 IEEECongress on Evolutionary Computation, CEC 2008; 4630935; 1110; 1116; 10.1109/CEC.2008.4630935

160. Caponio, A., Neri, F., Cascella, G.L., Salvatore, N.; Application of Memetic Differential Evolution frameworks toPMSM drive design; 2008; 2008 IEEE Congress on Evolutionary Computation, CEC 2008; 4631079; 2113; 2120;10.1109/CEC.2008.4631079

161. Neri, F., Tirronen, V.; On Memetic Differential Evolution frameworks: A study of advantages and limitationsin hybridization; 2008; 2008 IEEE Congress on Evolutionary Computation, CEC 2008; 4631082; 2135; 2142;10.1109/CEC.2008.4631082

162. Mandal, K.K., Chakraborty, N.; Effect of control parameters on differential evolution based combined economicemission dispatch with valve-point loading and transmission loss; 2008; International Journal of Emerging ElectricPower Systems; 9; 4; 5;

163. Storn, R.; Differential evolution research - Trends and open questions; 2008; Studies in Computational Intelligence;143; 1; 31; 10.1007/978-3-540-68830-3 1

164. Nearchou, A.C.; A differential evolution approach for the common due date early/tardy job scheduling problem;2008; Computers and Operations Research; 35; 4; 1329; 1343; 10.1016/j.cor.2006.08.013

165. Noman, N., Iba, H.; Accelerating differential evolution using an adaptive local search; 2008; IEEE Transactionson Evolutionary Computation; 12; 1; 107; 125; 10.1109/TEVC.2007.895272

166. Tvrdik, J.; Exponential crossover in competitive differential evolution; 2008; MENDEL 2008 - 14th InternationalConference on Soft Computing: Evolutionary Computation, Genetic Programming, Fuzzy Logic, Rough Sets,Neural Networks, Fractals, Bayesian Methods; ; 44; 49;

15 of 43

167. Qing, A.; A parametric study on differential evolution based on benchmark electromagnetic inverse scatter-ing problem; 2007; 2007 IEEE Congress on Evolutionary Computation, CEC 2007; ; 4424706; 1904; 1909;10.1109/CEC.2007.4424706

168. Yang, Z., Tang, K., Yao, X.; Differential evolution for high-dimensional function optimization; 2007; 2007 IEEECongress on Evolutionary Computation, CEC 2007; ; 4424929; 3523; 3530; 10.1109/CEC.2007.4424929

169. Tvrdik, J., Kriv?, I.; Competitive self-adaptation in evolutionary algorithms; 2007; New Dimensions in FuzzyLogic and Related Technologies - Proceedings of the 5th EUSFLAT 2005 Conference; 2; ; 251; 258;

170. Omran, M.G.H., Engelbrecht, A.P., Salman, A.; Self-adaptive barebones differential evolution; 2007; 2007 IEEECongress on Evolutionary Computation, CEC 2007; ; 4424834; 2858; 2865; 10.1109/CEC.2007.4424834

171. Nearchou, A.C.; Balancing large assembly lines by a new heuristic based on differential evolution method; 2007;International Journal of Advanced Manufacturing Technology; 34; 09.oct; 1016; 1029; 10.1007/s00170-006-0655-7

172. Kaelo, P., Ali, M.M.; Differential evolution algorithms using hybrid mutation; 2007; Computational Optimizationand Applications; 37; 2; 231; 246; 10.1007/s10589-007-9014-3

173. Tvrdik, J.; Adaptation in differential evolution: A comparison on composition test functions; 2007; Mendel;2007-January; January; 1; 6;

174. Nearchou, A.C.; An Efficient Meta-Heuristic for the Single Machine Common Due Date Scheduling Problem; 2006;Intelligent Production Machines and Systems - 2nd I*PROMS Virtual International Conference 3-14 July 2006; ;431; 435; 10.1016/B978-008045157-2/50077-8

175. Kukkonen, S., Lampinen, J.; An empirical study of control parameters for the third version of Generalized Differ-ential Evolution (GDE3); 2006; 2006 IEEE Congress on Evolutionary Computation, CEC 2006; ; 1688553; 2002;2009;

176. Price, K.V., Ronkkonen, J.I.; Comparing the uni-modal scaling performance of global and local selection in amutation-only differential evolution algorithm; 2006; 2006 IEEE Congress on Evolutionary Computation, CEC2006; ; 1688557; 2034; 2041;

177. Nearchou, A.C., Omirou, S.L.; Differential evolution for sequencing and scheduling optimization; 2006; Journal ofHeuristics; 12; 6; 395; 411; 10.1007/10732-006-3750-x

178. Tvrdik, J.; Differential evolution: Competitive setting of control parameters; 2006; Proceedings of the InternationalMulticonference on Computer Science and Information Technology, IMCSIT; 1; ; 207; 213;

179. Ali, M.M., Fatti, L.P.; A differential free point generation scheme in the differential evolution algorithm; 2006;Journal of Global Optimization; 35; 4; 551; 572; 10.1007/s10898-005-3767-y

180. Nearchou, A.C.; Meta-heuristics from nature for the loop layout design problem; 2006; International Journal ofProduction Economics; 101; 2; 312; 328; 10.1016/j.ijpe.2005.02.001

181. Kaelo, P., Ali, M.M.; A numerical study of some modified differential evolution algorithms; 2006; European Journalof Operational Research; 169; 3; 1176; 1184; 10.1016/j.ejor.2004.08.047

182. Tvrdik, J.; Competitive differential evolution; 2006; Mendel; 2006-January; January; 7; 12;

183. Ronkkonen, J., Kukkonen, S., Price, K.V.; Real-parameter optimization with differential evolution; 2005; 2005IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; 1; ; 506; 513;

184. Liu, J., Lampinen, J.; A fuzzy adaptive differential evolution algorithm; 2005; Soft Computing; 9; 6; 448; 462;10.1007/s00500-004-0363-x

185. Schmidt, H., Thierauf, G.; A combined heuristic optimization technique; 2005; Advances in Engineering Software;36; 1; 11; 19; 10.1016/j.advengsoft.2003.12.001

186. Fan, H.-Y., Lampinen, J.; A trigonometric mutation operation to differential evolution; 2003; Journal of GlobalOptimization; 27; 1; 105; 129; 10.1023/A:1024653025686

16 of 43

Citări pentru D. Zaharie - D. Zaharie - Parameter Adaptation in Differential Evolution by Controlling thePopulation Diversity, Analele Univ. Timisoara (Proc. of SYNASC 2002), vol. XXXX, issue 2, 2002

1. Sallam, K.M., Elsayed, S.M., Sarker, R.A., Essam, D.L.; Two-phase differential evolution framework for solvingoptimization problems; 2017; 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016; 7850258; ;10.1109/SSCI.2016.7850258

2. Hu, Z., Su, Q., Yang, X., Xiong, Z.; Not guaranteeing convergence of differential evolution on a class of multimodalfunctions; 2016; Applied Soft Computing Journal; 41; 479; 487; 4; 10.1016/j.asoc.2016.01.001

3. Fan, Q., Zhang, Y.; Self-adaptive differential evolution algorithm with crossover strategies adaptation and itsapplication in parameter estimation; 2016; Chemometrics and Intelligent Laboratory Systems; 151; 164; 171; 3;10.1016/j.chemolab.2015.12.020

4. Raghu, R., Jeyakumar, G.; Mathematical modelling of migration process to measure population diversity of Dis-tributed Evolutionary Algorithms; 2016; Indian Journal of Science and Technology; 9; 31; 82410; ; 10.17485/ijst/2016/v9i31/82410

5. Fan, Q., Yan, X.; Self-adaptive differential evolution algorithm with zoning evolution of control parameters andadaptive mutation strategies; 2016; IEEE Transactions on Cybernetics; 46; 1; 7057674; 219; 232; 3; 10.1109/TCYB.2015.2399478

6. Kozlov, K., Chebotarev, D., Hassan, M., Triska, M., Triska, P., Flegontov, P., Tatarinova, T.V.; DifferentialEvolution approach to detect recent admixture; 2015; BMC Genomics; 16; 8; S9; 3; 10.1186/1471-2164-16-S8-S9

7. Fan, Q., Yan, X.; Self-adaptive differential evolution algorithm with discrete mutation control parameters; 2015;Expert Systems with Applications; 42; 3; 1551; 1572; 17; 10.1016/j.eswa.2014.09.046


9. Thangavelu, S., Jeyakumar, G., Shunmuga Velyautham, C.; Population variance based empirical analysis ofthe behavior of differential evolution variants; 2015; Applied Mathematical Sciences; 9; 65-68; 3249; 3263; 1;10.12988/ams.2015.54312

10. Raghu, R., Jeyakumar, G.; Empirical analysis on the population diversity of the sub-population in distributeddifferential evolution algorithm; 2015; International Journal of Control Theory and Applications; 8; 5; 1809; 1816;

11. Draa, A., Bouzoubia, S., Boukhalfa, I.; A sinusoidal differential evolution algorithm for numerical optimisation;2015; Applied Soft Computing Journal; 27; 99; 126; 14; 10.1016/j.asoc.2014.11.003

12. Suganthan, P.N., Das, S., Mukherjee, S., Chatterjee, S.; Adaptation methods in differential evolution: A review;2014; Mendel; 2014-January; January; 131; 140;

13. Kozlov, K., Ivanisenko, N., Ivanisenko, V., Kolchanov, N., Samsonova, M., Samsonov, A.M.; Enhanced differentialevolution entirely parallel method for biomedical applications; 2013; Lecture Notes in Computer Science ; 7979LNCS; 409; 416; 10.1007/978-3-642-39958-9-37

14. Lian, H., Qin, Y., Liu, J.; An Adaptive Differential Evolution Algorithm Based on New Diversity; 2013; Interna-tional Journal of Computational Intelligence Systems; 6; 6; 1094; 1107; 1; 10.1080/18756891.2013.816064

15. Iacca, G.; Distributed optimization in wireless sensor networks: An island-model framework; 2013; Soft Computing;17; 12; 2257; 2277; 3; 10.1007/s00500-013-1091-x

16. Hu, Z., Xiong, S., Su, Q., Zhang, X.; Sufficient conditions for global convergence of differential evolution algorithm;2013; Journal of Applied Mathematics; 2013; 193196; 7; 10.1155/2013/193196

17. Weber, M., Neri, F., Tirronen, V.; A study on scale factor/crossover interaction in distributed differential evolution;2013; Artificial Intelligence Review; 39; 3; 195; 224; 11; 10.1007/s10462-011-9267-1

18. De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.; A model based on biological invasions forisland evolutionary algorithms; 2012; Lecture Notes in Computer Science ; 7401 LNCS; 157; 168; 1; 10.1007/978-3-642-35533-2 14

19. De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.; Biological invasion-inspired migration indistributed evolutionary algorithms; 2012; Information Sciences; 207; 50; 65; 21; 10.1016/j.ins.2012.04.027

20. Rama Mohan Rao, A., Lakshmi, K., Venkatachalam, D.; Damage diagnostic technique for structural health mon-itoring using POD and self adaptive differential evolution algorithm; 2012; Computers and Structures; 106-107;228; 244; 10; 10.1016/j.compstruc.2012.05.009

21. Ghosh, S., Das, S., Vasilakos, A.V., Suresh, K.; On convergence of differential evolution over a class of continuousfunctions with unique global optimum; 2012; IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics; 42; 1; 5961644; 107; 124; 42; 10.1109/TSMCB.2011.2160625

22. Du Plessis, M.C., Engelbrecht, A.P.; Self-adaptive competitive differential evolution for dynamic environments;2011; IEEE SSCI 2011 - Symposium Series on Computational Intelligence - SDE 2011: 2011 IEEE Symposium onDifferential Evolution; 5952054; 41; 48; 4; 10.1109/SDE.2011.5952054

23. Kozlov, K., Samsonov, A.; DEEP-differential evolution entirely parallel method for gene regulatory networks; 2011;Journal of Supercomputing; 57; 2; 172; 178; 11; 10.1007/s11227-010-0390-617 of 43

24. Weber, M., Neri, F., Tirronen, V.; A study on scale factor in distributed differential evolution; 2011; InformationSciences; 181; 12; 2488; 2511; 63; 10.1016/j.ins.2011.02.008

25. Das, S., Suganthan, P.N.; Differential evolution: A survey of the state-of-the-art; 2011; IEEE Transactions onEvolutionary Computation; 15; 1; 5601760; 4; 31; 1552; 10.1109/TEVC.2010.2059031

26. Weber, M., Tirronen, V., Neri, F.; Scale factor inheritance mechanism in distributed differential evolution; 2010;Soft Computing; 14; 11; 1187; 1207; 74; 10.1007/s00500-009-0510-5

27. Weber, M., Neri, F., Tirronen, V.; Parallel random injection differential evolution; 2010; Lecture Notes in ComputerScience ; 6024 LNCS; PART 1; 471; 480; 5; 10.1007/978-3-642-12239-2-49

28. Weber, M., Neri, F., Tirronen, V.; Distributed differential evolution with explorative-exploitative populationfamilies; 2009; Genetic Programming and Evolvable Machines; 10; 4; 343; 371; 60; 10.1007/s10710-009-9089-y

29. Kozlov, K., Samsonov, A.; DEEP - Differential evolution entirely parallel method for gene regulatory networks;2009; Lecture Notes in Computer Science ; 5698 LNCS; 126; 132; 1; 10.1007/978-3-642-03275-2 13

30. Reynoso-Meza, G., Sanchis, J., Blasco, X.; An adaptive parameter control for the differential evolution algorithm;2009; Lecture Notes in Computer Science ; 5517 LNCS; PART 1; 375; 382; 3; 10.1007/978-3-642-02478-8 47

31. Zielinski, K., Wang, X., Laur, R.; Comparison of adaptive approaches for differential evolution; 2008; LectureNotes in Computer Science ; 5199 LNCS; 641; 650; 13; 10.1007/978-3-540-87700-4 64

32. Omran, M.G.H., Engelbrecht, A.P., Salman, A.; Self-adaptive barebones differential evolution; 2007; 2007 IEEECongress on Evolutionary Computation, CEC 2007; 4424834; 2858; 2865; 3; 10.1109/CEC.2007.4424834

33. Zielinski, K., Weitkemper, P., Laur, R., Kammeyer, K.-D.; Parameter study for differential evolution using a powerallocation problem including interference cancellation; 2006; 2006 IEEE Congress on Evolutionary Computation,CEC 2006; 1688533; 1857; 1864; 83;

34. Omran, M.G.H., Salman, A., Engelbrecht, A.P.; Self-adaptive differential evolution; 2005; Lecture Notes in Com-puter Science ; 3801 LNAI; 192; 199; 158; 10.1007/11596448 28

35. Liu, J., Lampinen, J.; A fuzzy adaptive differential evolution algorithm; 2005; Soft Computing; 9; 6; 448; 462; 470;10.1007/s00500-004-0363-x

36. Madavan, N.K.; On improving efficiency of differential evolution for aerodynamic shape optimization applications;2004; Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference;6; 3590; 3609; 2;

37. Dragoi, E.-N., Dafinescu, V.; Parameter control and hybridization techniques in differential evolution: a survey;2016; Artificial Intelligence Review; 45; 4; 447; 470; 1; 10.1007/s10462-015-9452-8

18 of 43

Citări pentru D. Zaharie - Control of Population Diversity and Adaptation in Differential Evolution Algorithms,in R. Matousek, P. Osmera (eds.), Proc. of Mendel 2003, 9th International Conference on Soft Computing,Brno, Czech Republic, june 2003, pp. 41-46, 2003.

1. Santhosh, E.C., Sangwai, J.S.; A hybrid differential evolution algorithm approach towards assisted history matchingand uncertainty quantification for reservoir models; 2016; Journal of Petroleum Science and Engineering; 142; 21;35; 10.1016/j.petrol.2016.01.038

2. Dragoi, E.-N., Dafinescu, V.; Parameter control and hybridization techniques in differential evolution: a survey;2016; Artificial Intelligence Review; 45; 4; 447; 470; 10.1007/s10462-015-9452-8

3. Cavalini, A.A., Lobato, F.S., Koroishi, E.H., Steffen, V.; Model updating of a rotating machine using the self-adaptive differential evolution algorithm; 2016; Inverse Problems in Science and Engineering; 24; 3; 504; 523;10.1080/17415977.2015.1047364

4. Habibi Davijani, M., Banihabib, M.E., Nadjafzadeh Anvar, A., Hashemi, S.R.; Multi-Objective OptimizationModel for the Allocation of Water Resources in Arid Regions Based on the Maximization of Socioeconomic Effi-ciency; 2016; Water Resources Management; 30; 3; 927; 946; 10.1007/s11269-015-1200-y

5. Wu, G., Mallipeddi, R., Suganthan, P.N., Wang, R., Chen, H.; Differential evolution with multi-population basedensemble of mutation strategies; 2016; Information Sciences; 329; 329; 345; 10.1016/j.ins.2015.09.009

6. Habibi Davijani, M., Banihabib, M.E., Nadjafzadeh Anvar, A., Hashemi, S.R.; Optimization model for the allo-cation of water resources based on the maximization of employment in the agriculture and industry sectors; 2016;Journal of Hydrology; 533; 430; 438; 10.1016/j.jhydrol.2015.12.025

7. Park, S.-Y., Lee, J.-J.; Stochastic Opposition-Based Learning Using a Beta Distribution in Differential Evolution;2016; IEEE Transactions on Cybernetics; 46; 10; 7270303; 2184; 2194; 10.1109/TCYB.2015.2469722

8. Sharma, H., Sharma, V.P., Sharma, A., Meena, P.; An improved fitness based differential evolution algorithm (IFB-DEA); 2016; ACM International Conference Proceeding Series; 04-05-March-2016; a101;

Date post:	18-Oct-2019
Category:	Documents
Author:	others
View:	20 times
Download:	0 times

Daniela Zaharie -...

Documents

teze doctorat 2006.pdf

teze de doctorat - cluj

CV-Munteanu-Valentin - doctorat.uvt.rodoctorat.uvt.ro/wp-content/uploads/2018/11/CV_VAlentin_Munteanu...Semlnaril: Managementul producÿiel, Management In telecomunicaÿli Unlversltatea

Raport Teze Doctorat IFA

Bad Curriculum vitae Europass - doctorat.uvt.rodoctorat.uvt.ro/wp-content/uploads/2019/10/CV-Constantin-Stanciu.pdfInstrument principal-viola, muzica de camera , metodica predarii

Rezultate teze subiect unic Pascani- romana

Teze an scolar 2016-2017

doctorat.uvt.rodoctorat.uvt.ro/wp-content/uploads/2017/04/raport-Ileana-Teleaga.pdf · universitatea de vest din timisoara consiliul studiilor universitare de doctorat universitatea

Teze de doctorat 2008

Teze de doctorat susţinute în cadrul UNMB 2009

Teze de doctorat susţinute în cadrul UNMB 2012

teze doctorat

Masurarea Elementelor M Zaharie

Teze de doctorat susţinute în cadrul UNMB 2011

Teze de doctorat 2009

doctorat.uvt.rodoctorat.uvt.ro/wp-content/uploads/2017/11/CV-Popa-Nicolae.pdf · Geografie socialä si culturalä, Dezvoltare durabilä regionalä localä, Geografie politicä si

Curriculum vitae Europass - doctorat.uvt.rodoctorat.uvt.ro/wp-content/uploads/2018/12/CV_Cristian_Iftode-Timisoara.pdf · Retorică și argumentare (master: Tehnici diplomatice, Facultatea

Universitatea de Vest Timisoara - doctorat.uvt.rodoctorat.uvt.ro/wp-content/uploads/2019/04/B02.-Propunere-Regulament...științelor politice, drepturile omului, relații internaționale,