citas a publicaciones del dr. carlos a. coello coello que ......citas a publicaciones del dr. carlos...

Citas a Publicaciones delDr. Carlos A. Coello Coello

que aparecen en el Citation Index

December 18, 2008

Tesis Doctoral• Carlos A. Coello Coello. An Empirical Study of Evolutionary Techniques for

Multiobjective Optimization in Engineering Design, PhD thesis, Departmentof Computer Science, Tulane University, New Orleans, Louisiana, April1996.

1. Min-Rong Chen, Yong-zai Lu and Gen-ke Yang, “Multiobjective extremaloptimization with applications to engineering design”, Journal of ZhejiangUniversity SCIENCE A, Vol. 8, No. 12, pp. 1905–1911, November 2007.

2. C. Elegbede, “Structural reliability assessment based on particles swarmoptimization”, Structural Safety, Vol. 27, No. 2, pp. 171–186, 2005.

3. Adil Baykasoglu, “Preemptive goal programming using simulated anneal-ing”, Engineering Optimization, Vol. 37, No. 1, pp. 49–63, January 2005.

4. Guan-Chun Luh and Chung-Huei Chueh, “Multi-objective optimal designof truss structure with immune algorithm”, Computers & Structures, Vol.82, Nos. 11–12, pp. 829–844, May 2004.

5. J. Oh and C. Wu, “Genetic-algorithm-based real-time task scheduling withmultiple goals”, Journal of Systems and Software, Vol. 71, No. 3, pp.245–258, May 2004.

6. C. Elegbede and K. Adjallah, “Availability allocation to repairable systemswith genetic algorithms: a multi-objective formulation”, Reliability Engi-neering & System Safety, Vol. 82, No. 3, pp. 319–330, December 2003.

7. Balram Suman, “Simulated Annealing-Based Multiobjective Algorithmsand Their Application for System Reliability”, Engineering Optimization,Vol. 35, No. 4, pp. 391–416, August 2003.

8. R.F. Coelho, H. Bersini and P. Bouillard, “Parametrical mechanical designwith constraints and preferences: application to a purge valve”, ComputerMethods in Applied Mechanics and Engineering, Vol. 192, Nos. 39–40,pp. 4355–4378, 2003.

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9. B. De Smedt and G.C.E. Gielen, “WATSON: Design space boundary ex-ploration and model generation for analog and RF IC design”, IEEE Trans-actions on Computer-Aided Design of Integrated Circuits and Systems, Vol.22, No. 2, pp. 213–224, February 2003.

10. Johan Andersson and David Wallace, “Pareto optimization using the strug-gle genetic crowding algorithm”, Engineering Optimization, Vol. 34, No.6, pp. 623–643, December 2002.

11. Guan-Chun Luh, Chung-Huei Chueh and Wei-Wen Liu, “MOIA: Multi-Objective Immune Algorithm”, Engineering Optimization, Volume 35, No.2, pp. 143–164, April 2003.

12. K.C. Tan, E.F. Khor, T.H. Lee and Y.J. Yang, “A tabu-based exploratoryevolutionary algorithm for multiobjective optimization”, Artificial Intelli-gence Review, Vol. 19, No. 3, pp. 231–260, May 2003.

13. K.C. Tan, E.F. Khor, T.H. Lee and R. Sathikannan, “An evolutionary algo-rithm with advanced goal and priority specification for multi-objective op-timization”, Journal of Artificial Intelligence Research, Vol. 18, pp. 183–215, 2003.

14. K.C. Tan, T.H. Lee and E.F. Khor, “Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons”, Ar-tificial Intelligence Review, Vol. 17, No. 4, pp. 253–290, June 2002.

15. K.C. Tan, T.H. Lee & E. F. Khor, “Evolutionary Algorithms with DynamicPopulation Size and Local Exploration for Multiobjective Optimization,IEEE Transactions on Evolutionary Computation, Vol. 5, No. 6, pp. 565-588, December 2001.

16. A. Baykasoglu, “Goal programming using multiple objective tabu search”,Journal of the Operational Research Society, Vol. 52, No. 12, pp. 1359–1369, December 2001.

17. K.C. Tan, Tong H. Lee, D. Khoo & E.F. Khor, “A Multiobjective Evolution-ary Algorithm Toolbox for Computer-Aided Multiobjective Optimization”,IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernet-ics, Vol. 31, No. 4, pp. 537–556, August 2001.

18. Johan Andersson and Peter Krus, “Multiobjective Optimization of MixedVariable Design Problems”, en Eckart Zitzler, Kalyanmoy Deb, LotharThiele, Carlos A. Coello Coello & David Corne (Eds.), First InternationalConference on Evolutionary Multi-Criterion Optimization, Springer-Verlag,Zurich, Suiza, pp. 624–638, Marzo de 2001.

19. Matthias Ehrgott and Xavier Gandibleux, “A Survey and Annotated Bib-liography of Multiobjective Combinatorial Optimization”, OR Spektrum,Vol. 22, pp. 425–460, 2000.

20. B. Suman, “Study of self-stopping PDMOSA and performance measurein multiobjective optimization”, Computers & Chemical Engineering, Vol.29, No. 5, pp. 1131–1147, April 15, 2005.

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21. J. Olvander, “Robustness considerations in multi-objective optimal design”,Journal of Engineering Design, Vol. 16, No. 5, pp. 511–523, October2005.

22. M. Omran, A.P. Engelbrecht and A. Salman, “Particle swarm optimizationmethod for image clustering”, International Journal of Pattern Recognitionand Artificial Intelligence, Vol. 19, No. 3, pp. 297–321, May 2005.

23. David Greiner, Gabriel Winter, Jose M. Emperador and Blas Galvan, “GrayCoding in Evolutionary Multicriteria Optimization: Application in FrameStructural Optimum Design”, in Carlos A. Coello Coello, Arturo HernandezAguirre and Eckart Zitzler (editors), Evolutionary Multi-Criterion Opti-mization. Third International Conference, EMO 2005, pp. 576–591, Springer.Lecture Notes in Computer Science Vol. 3410, Guanajuato, Mexico, March2005.

24. Seyed Hamid Reza Pasandideh and Seyed Taghi Akhavan Niaki, “Multi-response simulation optimization using genetic algorithm within desirabil-ity function framework”, Applied Mathematics and Computation, Vol. 175,No. 1, pp. 366–382, April 1, 2006.

25. B. Suman and P. Kumar, “A survey of simulated annealing as a tool for sin-gle and multiobjective optimization”, Journal of the Operational ResearchSociety, Vol. 57, No. 10, pp. 1143–1160, October 2006.

Libros• Coello Coello, Carlos A.; Van Veldhuizen, David A. & Lamont, Gary B.

“Evolutionary Algorithms for Solving Multi-Objective Problems”, KluwerAcademic Publishers, New York, ISBN 0-3064-6762-3, May 2002.o Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen,“Evolutionary Algorithms for Solving Multi-Objective Problems”, SecondEdition, Springer-Verlag, New York, Septiembre 2007, ISBN 978-0-387-33254-3.

1. Xiufen Zou, Yu Chen, Minzhong Liu and Lishan Kang, “A New Evolution-ary Algorithm for Solving Many-Objective Optimization Problems”, IEEETransactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol.38, No. 5, pp. 1402–1412, October 2008.

2. N. Chakraborti, A. Shekhar, A. Singhal, S. Chakraborty, S. Chowdhuryand R. Sripriya, “Fluid flow in hydrocyclones optimized through multi-objective genetic algorithms”, Inverse Problems in Science and Engineer-ing, Vol. 16, No. 8, pp. 1023–1046, December 2008 (JOUR INT).

3. Min Zhang, Wenjian Luo and Xufa Wang, “Differential evolution with dy-namic stochastic selection for constrained optimization”, Information Sci-ences, Vol. 178, No. 15, pp. 3043–3074, August 1, 2008.

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4. Elizabeth F. Wanner, Frederico G. Guimaraes, Ricardo H.C. Takahashiand Peter J. Fleming, “Local Search with Quadratic Approximations intoMemetic Algorithms for Optimization with Multiple Criteria”, Evolution-ary Computation, Vol. 16, No. 2, pp. 185–224, Summer 2008.

5. Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabe Dorronsoro,Juan J. Durillo and Andreas Beham, “AbYSS: Adapting Scatter Search toMultiobjective Optimization”, IEEE Transactions on Evolutionary Com-putation, Vol. 12, No. 4, pp. 439–457, August 2008.

6. Frederic Ros, Serge Guillaume, Marco Pintore and Jacques R. Chretien,“Hybrid genetic algorithm for dual selection”, Pattern Analysis and Appli-cations, Vol. 11, No. 2, pp. 179–198, June 2008.

7. Maoguo Gong, Licheng Jiao, Haifeng Du and Liefeng Bo, “Multiobjectiveimmune algorithm with nondominated neighbor-based selection”, Evolu-tionary Computation, Vol. 16, No. 2, pp. 225–255, Summer 2008.

8. Mohammed Khabzaoui, Clarisse Dhaenens and El-Ghazali Talbi, “Com-bining evolutionary algorithms and exact approaches for multi-objectiveknowledge discovery”, RAIRO–Operations Research, Vol. 42, No. 1, pp.69–83, January-March 2008.

9. J. Reca, J. Martinez, R. Banos and C. Gil, “Optimal design of gravity-fed looped water distribution networks considering the resilience index”,Journal of Water Resources Planning and Management–ASCE, Vol. 134,No. 3, pp. 234–238, May-June 2008.

10. Sanghamitra Bandyopadhyay, Sriparna Saha, Ujjwal Maulik and Kalyan-moy Deb, “A Simulated Annealing-Based Multiobjective Optimization Al-gorithm: AMOSA”, IEEE Transactions on Evolutionary Computation, Vol.12, No. 3, pp. 269–283, June 2008.

11. N. Chakraborti, B. Siva Kumar, V. Satish Babu, S. Moitra and A. Mukhopad-hyay, “A new multi-objective genetic algorithm applied to hot-rolling pro-cess”, Applied Mathematical Modelling, Vol. 32, No. 9, pp. 1781–1789,September 2008.

12. Hamidreza Eskandari and Christopher D. Geiger, “A fast Pareto genetic al-gorithm approach for solving expensive multiobjective optimization prob-lems”, Journal of Heuristics, Vol. 14, No. 3, pp. 203–241, June 2008.

13. Philipp Limbourg and Hans-Dieter Kochs, “Multi-objective optimizationof generalized reliability design problems using feature models - A conceptfor early design stages”, Reliability Engineering & System Safety, Vol. 93,No. 6, pp. 815–828, June 2008.

14. Jun Guo, Yi Wang, Kit-Sang Tang, Sammy Chan, Eric W.M. Wong, PeterTaylor and Moshe Zukerman, “Evolutionary optimization of file assign-ment for a large-scale video-on-demand system”, IEEE Transactions onKnowledge and Data Engineering, Vol. 20, No. 6, pp. 836–850, June2008.

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15. Sai-Ho Yeung, Hoi-Kuen Ng and Kim-Fung Man, “Multi-criteria designmethodology of a dielectric resonator antenna with jumping genes evolu-tionary algorithm”, AEU-International Journal of Electronics and Commu-nications, Vol. 62, No. 4, pp. 266–276, 2008.

16. David S. Robin, Wan Weishi Wan, Fernando Sannibale and Victor P. Suller,“Global analysis of all linear stable settings of a storage ring lattice”, Physi-cal Review Special Topics–Accelerators and Beams, Vol. 11, No. 2, ArticleNumber 024002, February 2008.

17. Nicolas Jozefowiez, Frederic Semet and El-Ghazali Talbi, “Multi-objectivevehicle routing problems”, European Journal of Operational Research,Vol. 189, No. 2, pp. 293–309, September 1, 2008.

18. Miguel Delgado, Manuel P. Cuellar and Maria Carmen Pegalajar, “Multi-objective hybrid optimization and training of recurrent neural Networks”,IEEE Transactions on Systems, Man, and Cybernetics–Part B: Cybernet-ics, Vol. 38, No. 2, pp. 381–403, April 2008.

19. Xingdong Zhang and Marc P. Armstrong, “Genetic algorithms and the cor-ridor location problem: multiple objectives and alternative solutions”, En-vironment and Planning B–Planning & Design, Vol. 35, No. 1, pp. 148–168, January 2008.

20. Wasim Raza and Kwang-Yong Kim, “Multiobjective optimization of a wire-wrapped LMR fuel assembly”, Nuclear Technology, Vol. 162, No. 1, pp.45–52, April 2008.

21. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Ro-bust identification of non-linear greenhouse model using evolutionary al-gorithms”, Control Engineering Practice, Vol. 16, No. 5, pp. 515–530,May 2008.

22. Ben Torben-Nielsen, Karl Tuyls and Eric Postma, “EvOL-NEURON: Neu-ronal morphology generation”, Neurocomputing, Vol. 71, Nos. 4–6, pp.963–972, January 2008.

23. Fabian Duddeck, “Multidisciplinary optimization of car bodies”, Structuraland Multidisciplinary Optimization, Vol. 35, No. 4, pp. 375–389, April2008.

24. Ricardo Perera and Antonio Ruiz, “A multistage FE updating procedure fordamage identification in large-scale structures based on multiobjective evo-lutionary optimization”, Mechanical Systems and Signal Processing, Vol.22, No. 4, pp. 970–991, May 2008.

25. Gio J. Kao and Sheldon H. Jacobson, “Finding preferred subsets of Paretooptimal solutions”, Computational Optimization and Applications, Vol. 40,No. 1, pp. 73–95, May 2008.

26. A. I. Olcer, “A hybrid approach for multi-objective combinatorial optimi-sation problems in ship design and shipping”, Computers & OperationsResearch, Vol. 35, No. 9, pp. 2760–2775, September 2008.

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27. Hisao Ishibuchi, Kaname Narukawa, Noritaka Tsukamoto and Yusuke No-jima, “An empirical study on similarity-based mating for evolutionary mul-tiobjective combinatorial optimization”, European Journal of OperationalResearch, Vol. 188, No. 1, pp. 57–75, July 1, 2008.

28. Annette Muetze, “A neglected stepchild”, IEEE Industry Applications Mag-azine, Vol. 14, No. 2, pp. 14–22, March-April 2008.

29. N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali and A. Habib-doust, “Modelling and Pareto optimization of heat transfer and flow coef-ficients in microchannels using GMDH type neural networks and geneticalgorithms”, Energy Conversion and Management, Vol. 49, No. 2, pp.311–325, February 2008.

30. N. Nariman-zadeh, A. Jamali and A. Hajiloo, “Frequency-based reliabilityPareto optimum design of proportional-integral-derivative controllers forsystems with probabilistic uncertainty”, Proceedings of the Institution ofMechanical Engineers Part I–Journal of Systems and Control Engineering,Vol. 221, No. I8, pp. 1061–1075, December 2007.

31. Shubhabrata Datta, Frank Pettersson, Subhas Ganguly, Henrik Saxen andNirupam Chakraborti, “Identification of factors governing mechanical prop-erties of TRIP-aided steel using genetic algorithms and neural networks”,Materials and Manufacturing Processes, Vol. 23, No. 2, pp. 131–138,2008.

32. Bin Qian, Ling Wang, De-Xian Huang and Xiong Wang, “Scheduling multi-objective job shops using a memetic algorithm based on differential evolu-tion”, International Journal of Advanced Manufacturing Technology, Vol.35, Nos. 9–10, pp. 1014–1027, January 2008.

33. O. Giustolisi, A. Doglioni, D.A. Savic and F. di Pierro, “An evolution-ary multiobjective strategy for the effective management of groundwaterresources”, Water Resources Research, Vol. 44 No. 1, article numberW01403, January 3, 2008.

34. Eduardo Fernandez, Nora Cancela and Rafael Olmedo, “Deriving a finalranking from fuzzy preferences: An approach compatible with the Princi-ple of Correspondence”, Mathematical and Computer Modelling, Vol. 47,Nos. 1–2, pp. 218–234, January 2008.

35. Javier Sanchis, Miguel A. Martinez and Xavier Blasco, “Integrated multi-objective optimization and a priori preferences using genetic algorithms”,Information Sciences, Vol. 178, No. 4, pp. 931–951, February 15, 2008.

36. J. Sanchis, M. Martinez and X. Blasco, “Multi-objective engineering de-sign using preferences”, Engineering Optimization, Vol. 40, No. 3, pp.253–269, 2008.

37. Paulo Fazendeiro, Jose Valente de Oliveira and Witold Pedrycz, “A multi-objective design of a patient and anaesthetist-friendly neuromuscular block-ade controller”, IEEE Transactions on Biomedical Engineering, Vol. 54,No. 9, pp. 1667–1678, September 2007.

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38. Zbigniew Michalewicz and Matthew Michalewicz, “Machine intelligence,adaptive business intelligence, and natural intelligence”, IEEE Computa-tional Intelligence Magazine, Vol. 3, No. 1, pp. 54–63, 2008.

39. F. Pettersson, N. Chakraborti and S.B. Singh, “Neural Networks Analy-sis of Steel Plate Processing Augmented by Multi-objective Genetic Al-gorithms”, Steel Research International, Vol. 78, No. 12, pp. 890–898,December 2007.

40. Qingfu Zhang, Aimin Zhou and Yaochu Jin, “RM-MEDA: A RegularityModel-Based Multiobjective Estimation of Distribution Algorithm”, IEEETransactions on Evolutionary Computation, Vol. 12, No. 1, pp. 41–63,February 2008.

41. A. Gaspar-Cunha and J.A. Covas, “Robustness in multi-objective optimiza-tion using evolutionary algorithms”, Computational Optimization and Ap-plications, Vol. 39, No. 1, pp. 75–96, January 2008.

42. Genci Capi, “Multiobjective evolution of neural controllers and task com-plexity”, IEEE Transactions on Robotics, Vol. 23, No. 6, pp. 1225–1234,2007.

43. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Non-linear robust identification of a greenhouse model using multi-objectiveevolutionary algorithms”, Biosystems Engineering, Vol. 98, No. 3, pp.335–346, November 2007.

44. Ang Yang, Hussein A. Abbass and Ruhul Sarker, “Characterizing warfarein red teaming”, IEEE Transactions on Systems, Man, and Cybernetics,Part B–Cybernetics, Vol. 36, No. 2, pp. 268–285, April 2006.

45. Julian Molina, Manuel Laguna, Rafael Marti and Rafael Caballero, “SSPMO:A scatter tabu search procedure for non-linear multiobjective optimiza-tion”, INFORMS Journal on Computing, Vol. 19, No. 1, pp. 91–100,January 2007.

46. Robic C. Purshouse and Peter J. Fleming, “On the Evolutionary Optimiza-tion of Many Conflicting Objectives”, IEEE Transactions on EvolutionaryAlgorithms, Vol. 11, No. 6, pp. 770–784, December 2007.

47. Qingfu Zhang and Hui Li, “MOEA/D: A Multiobjective Evolutionary Al-gorithm Based on Decomposition”, IEEE Transactions on EvolutionaryComputation, Vol. 11, No. 6, pp. 712–731, December 2007.

48. Sebastian Ventura, Cristobal Romero, Amelia Zafra, Jose A. Delgado andCesar Hervas, “JCLEC: a Java framework for evolutionary computation”,Soft Computing, Vol. 12, No. 4, pp. 381–392, February 2008.

49. Joshua B. Kollat and Patrick Reed, “A framework for visually interactivedecision-making and design using evolutionary multi-objective optimiza-tion (VI(D)under-barEO)”, Environmental Modelling & Software, Vol. 22,No. 12, pp. 1691–1704, December 2007.

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50. Marco A. Panduro, Carlos A. Brizuela and David H. Covarrubias, “De-sign of electronically steerable linear arrays with evolutionary algorithms”,Applied Soft Computing, Vol. 8, No. 1, pp. 46–54, January 2008.

51. Kay-Soon Low and Tze-Shyan Wong, “A multiobjective genetic algorithmfor optimizing the performance of hard disk drive motion control system”,IEEE Transactions on Industrial Electronics, Vol. 54, No. 3, pp. 1716–1725, June 2007.

52. Arlene G. Smithson, Karim Hamza and Kazuhiro Saitou, “Design for exist-ing lines: Part and process plan optimization to best utilize existing produc-tion lines”, Journal of Computing and Information Science in Engineering,Vol. 7, No. 2, pp. 126–131, June 2007.

53. DaeEun Kim and Jaehong Park, “Application of adaptive control to thefluctuation of engine speed at idle”, Information Sciences, Vol. 177, No.16, pp. 3341–3355, August 15, 2007.

54. A.F. Gomez-Skarmeta, F. Jimenez and G. Sanchez, “Improving interpretabil-ity in approximative fuzzy models via multiobjective evolutionary algo-rithms”, International Journal of Intelligent Systems, Vol. 22, No. 9, pp.943–969, September 2007.

55. Sarbajit Pal, Pankaj Ganguly and P.K. Biswas, “Cubic Bezier approxima-tion of a digitized curve”, Pattern Recognition, Vol. 40, No. 10, pp. 2730–2741, October 2007.

56. Maria Jose del Jesus, Pedro Gonzalez, Francisco Herrera and Mikel Mesonero,“Evolutionary fuzzy rule induction process for subgroup discovery: A casestudy in marketing”, IEEE Transactions on Fuzzy Systems, Vol. 15, No. 4,pp. 578–592, August 2007.

57. Luciano Sanchez and Ines Couso, “Advocating the use of imprecisely ob-served data in genetic fuzzy systems”, IEEE Transactions on Fuzzy Sys-tems, Vol. 15, No. 4, pp. 551–562, August 2007.

58. A. Tarafder, G.P. Rangaiah and Ajay K. Ray, “A study of finding manydesirable solutions in multiobjective optimization of chemical processes”,Computers & Chemical Engineering, Vol. 31, No. 10, pp. 1257–1271,October 2007.

59. S. Dehuri, S. Patnaik, A. Ghosh and R. Mall, “Application of elitist multi-objective genetic algorithm for classification rule generation”, Applied SoftComputing, Vol. 8, No. 1, pp. 477–487, January 2008.

60. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “An evolutionary mul-tiobjective framework for structural damage localization and quantifica-tion”, Engineering Structures, Vol. 29, No. 10, pp. 2540–2550, October2007.

61. M. Ye and G. Zhouz, “A local genetic approach to multi-objective, facil-ity layout problems with fixed aisles”, International Journal of ProductionResearch, Vol. 45, No. 22, pp. 5243–5264, 2007.

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62. Guangtao Fu, David Butler and Soon-Thiam Khu, “Multiple objective opti-mal control of integrated urban wastewater systems”, Environmental Mod-elling & Software, Vol. 23, No. 2, pp. 225–234, February 2008.

63. Yuren Zhou and Jun He, “Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids”, Information ProcessingLetters, Vol. 104, No. 4, pp. 117–122, November 15, 2007.

64. David Midgley, Robert Marks and Dinesh Kunchamwar, “Building and as-surance of agent-based models: An example and challenge to the field”,Journal of Business Research, Vol. 60, No. 8, pp. 884–893, August 2007.

65. Shubhabrata Datta, Frank Pettersson, Subhas Ganguly, Henrik Saxen andNiruopam Chakraborti, “Designing High Strength Multi-phase Steel forImproved Strength-Ductility Balance Using Neural Networks and Multi-objective Genetic Algorithms”, ISIJ International, Vol. 47, No. 8, pp.1195–1203, 2007

66. Yandra and Hiroyuki Tamura, “A new multiobjective genetic algorithmwith heterogeneous population for solving flowshop scheduling problems”,International Journal of Computer Integrated Manufacturing, Vol. 20, No.5, pp. 465–477, 2007.

67. Frederico G. Guimaraes, Reinaldo M. Palhares, Felipe Campelo and Ha-jime Igarashi, “Design of mixed H-2/H infinity control systems using al-gorithms inspired by the immune system”, Information Sciences, Vol. 177,No. 20, pp. 4368–4386, October 15, 2007.

68. Kumara Sastry, D.D. Johnson, Alexis L. Thompson, David E. Goldberg,Todd J. Martinez, Jeff Leiding and Jane Owens, “Optimization of Semiem-pirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms:Accurate Photodynamics for Larger Molecules and Longer Time Scales”,Materials and Manufacturing Processes, Vol. 22, No. 5, pp. 553–561,2007.

69. Henrik Saxen, Frank Pettersson and Kiran Gunturu, “Evolving NonlinearTime-Series Models of the Hot Metal Silicon Content in the Blast Fur-nace”, Materials and Manufacturing Processes, Vol. 22, No. 5, pp. 577–584, 2007.

70. Kaisa Miettinen, “Using Interactive Multiobjective Optimization in Con-tinuous Casting of Steel”, Materials and Manufacturing Processes, Vol.22, No. 5, pp. 585–593, 2007.

71. S. Ganguly, S. Datta and N. Chakraborti, “Genetic algorithms in optimiza-tion of strength and ductility of low-carbon steels”, Materials and Manu-facturing Processes, Vol. 22, Nos. 5–6, pp. 650–658, 2007.

72. Eleni Aggelogiannaki and Haralarnbos Sarimveis, “Simulated annealingalgorithm for prioritized multiobjective optimization-implementation in anadaptive model predictive control configuration”, IEEE Transactions onSystems, Man, and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp.902–915, August 2007.

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73. Christian Gagne and Marc Parizeau, “Genetic engineering of hierarchicalfuzzy regional representations for handwritten character recognition”, In-ternational Journal on Document Analysis and Recognition, Vol. 8, No. 4,pp. 223–231, September 2006.

74. Don Jyh-Fu Jeng, Ikno Kim and Junzo Watada, “Bio-soft computing withfixed-length DNA to a group control optimization problem”, Soft Comput-ing, Vol. 12, No. 3, pp. 223–228, February 2008.

75. Antonio Pinto, Daniele Peri and Emilio F. Campana, “Multiobjective op-timization of a containership using deterministic particle swarm optimiza-tion”, Journal of Ship Research, Vol. 51, No. 3, pp. 217–228, September2007.

76. Jing Liu, Weicai Zhong and Licheng Hao, “An organizational evolution-ary algorithm for numerical optimization”, IEEE Transactions on Systems,Man and Cybernetics Part B–Cybernetics, Vol. 37, No. 4, pp. 1052–1064,August 2007.

77. L.F. Gonzalez, J. Periaux, L. Damp and K. Srinivas, “Evolutionary methodsfor multidisciplinary optimization applied to the design of UAV systems”,Engineering Optimization, Vol. 39, No. 7, pp. 773–795, October 2007.

78. Joern Mehnen, Thomas Michelitsch, Christian Lasarczyk and Thomas Bartz-Beielstein, “Multi-objective evolutionary design of mold temperature con-trol using DACE for parameter optimization”, International Journal of Ap-plied Electromagnetics and Mechanics, Vol. 25, Nos. 1–4, pp. 661–667,2007.

79. Pascal Cote, Lael Parrott and Robert Sabourin, “Multi-objective optimiza-tion of an ecological assembly model”, Ecological Informatics, Vol. 2, No.1, pp. 23–31, January 1, 2007.

80. Maria Joao Alves and Marla Almeida, “MOTGA: A multiobjective Tcheby-cheff based genetic algorithm for the multidimensional knapsack problem”,Computers & Operations Research, Vol. 34, No. 11, pp. 3458–3470,November 2007.

81. Mario Koppen, Katrin Franke and Raul Vicente-Garcia, “Tiny GAs for im-age processing applications”, IEEE Computational Intelligence Magazine,Vol. 1, No. 2, pp. 17–26, May 2006.

82. G. Li, M. Li, S. Azarm, J. Rambo and Y. Joshi, “Optimizing thermal de-sign of data center cabinets with a new multi-objective genetic algorithm”,Distributed and Parallel Databases, Vol. 21, Nos. 2–3, pp. 167–192, June2007.

83. Chandan Guria, Mohan Varma, Surya P. Mehrotra and Santosh K. Gupta,“Simultaneous optimization of the performance of flotation circuits andtheir simplification using the jumping gene adaptations of genetic algorithm-II: More complex problems”, International Journal of Mineral Processing,Vol. 79, No. 3, pp. 149–166, June 2006.

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84. Y. Tang, P.M. Reed and J.B. Kollat, “Parallelization strategies for rapid androbust evolutionary multiobjective optimization in water resources applica-tions”, Advances in Water Resources, Vol. 30, No. 3, pp. 335–353, March2007.

85. J.B. Kollat and P.M. Reed, “A computational scaling analysis of multiob-jective evolutionary algorithms in long-term groundwater monitoring ap-plications”, Advances in Water Resources, Vol. 30, No. 3, pp. 408–419,March 2007.

86. Ivan Blecic, Arnaldo Cecchini and Giuseppe A. Trunfio, “A decision sup-port tool coupling a causal model and a multi-objective genetic algorithm”,Applied Intelligence, Vol. 26, No. 2, pp. 125–137, April 2007.

87. J.W. Large, D.F. Jones and M. Tamiz, “Hyper-spherical inversion transfor-mations in multi-objective evolutionary optimization”, European Journalof Operational Research, Vol. 177, No. 3, pp. 1678–1702, March 16,2007.

88. Sahnan A. Khan and Andries P. Engelbrecht, “A new fuzzy operator andits application to topology design of distributed local area networks”, In-formation Sciences, Vol. 177, No. 13, pp. 2692–2711, July 1, 2007.

89. M.R. Gholamian, S.M.T. Fatemi Ghomi and M. Ghazanfari, “A hybridsystem for multiobjective problems - A case study in NP-hard problems”,Knowledge-Based Systems, Vol. 20, No. 4, pp. 426–436, May 2007.

90. Patrick Reed, Joshua B. Kollat and V.K. Devireddy, “Using interactivearchives in evolutionary multiobjective optimization: A case study for long-term groundwater monitoring design”, Environmental Modelling & Soft-ware, Vol. 22, No. 5, pp. 683–692, May 2007.

91. Carlos Gomes da Silva, Jose Figueira and Joao Clımaco, “Integrating par-tial optimization with scatter search for solving bi-criteria {0,1}-knapsackproblems”, European Journal of Operational Research, Vol. 177, No. 3,pp. 1656–1677, March 16, 2007.

92. M.R. Gholamian, S.M.T. Fatemi Ghomi and M. Ghazanfari, “A hybrid in-telligent system for multiobjective decision making problems”, Computersand Industrial Engineering, Vol. 51, No. 1, pp. 26–43, September 2006.

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299. Thomas Hanne, “A multiobjective evolutionary algorithm for approximat-ing the efficient set”, European Journal of Operational Research, Vol. 176,No. 3, pp. 1723–1734, February 1, 2007.

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303. F. Pettersson, N. Chakraborti and H. Saxen, “A genetic algorithms basedmulti-objective neural net applied to noisy blast furnace data”, Applied SoftComputing, Vol. 7, pp. 387–397, 2007.

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304. Dimo Brockhoff and Eckart Zitzler, “Are All Objectives Necessary? OnDimensionality Reduction in Evolutionary Multiobjective Optimization”,in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J.Merelo-Guervos, L. Darrell Whitley and Xin Yao (editors), Parallel Prob-lem Solving from Nature - PPSN IX, 9th International Conference, pp. 533–542, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik,Iceland, September 2006.

305. Hernan E. Aguirre and Kiyoshi Tanaka, “Working principles, behavior, andperformance of MOEAs on MNK-landscapes”, European Journal of Op-erational Research, Vol. 181, No. 3, pp. 1670–1690, 16 September, 2007.

306. Pradyumn Kumar Shukla and Kalyanmoy Deb, “On finding multiple Pareto-optimal solutions using classical and evolutionary generating methods” Eu-ropean Journal of Operational Research, Vol. 181, No. 3, pp. 1630–1652,16 September, 2007.

307. Hiroyuki Sato, Hernan E. Aguirre and Kiyoshi Tanaka, “Local dominanceand local recombination in MOEAs on 0/1 multiobjective knapsack prob-lems”, European Journal of Operational Research, Vol. 181, No. 3, pp.1708–1723, 16 September, 2007.

308. Julia Handl and Joshua Knowles, “An Evolutionary Approach to Multiob-jective Clustering”, IEEE Transactions on Evolutionary Computation, Vol.11, No. 1, pp. 56–76, February 2007.

309. Francesco di Pierro, Shoon-Thiam Khu and Dragan A. Savic, “An Investi-gation on Preference Order Ranking Scheme for Multiobjective Evolution-ary Optimization”, IEEE Transactions on Evolutionary Computation, Vol.11, No. 1, pp. 17–45, February 2007.

310. C. Garcıa-Martınez, O. Cordon and F. Herrera, “A taxonomy and an empir-ical analysis of multiple objective ant colony optimization algorithms forthe bi-criteria TSP”, European Journal of Operational Research, Vol. 180,No. 1, pp. 116–148, July 1, 2007.

311. Darıo Maravall and Javier de Lope, “Multi-objective dynamic optimizationwith genetic algorithms for automatic parking”, Soft Computing, Vol. 11,No. 3, pp. 249–257, February 2007.

312. J.K.L. Wong, A.J. Mason, M.J. Neve and K.W. Sowerby, “Base stationplacement in indoor wireless systems using binary integer programming”,IEE Proceedings—Communications, Vol. 153, No. 5, pp. 771–778, Octo-ber 2006.

313. L. Araujo, “Multiobjective Genetic Programming for Natural LanguageParsing and Tagging”, in Thomas Philip Runarsson, Hans-Georg Beyer,Edmund Burke, Juan J. Merelo-Guervos, L. Darrell Whitley and Xin Yao(editors), Parallel Problem Solving from Nature - PPSN IX, 9th Interna-tional Conference, pp. 433–442, Springer. Lecture Notes in ComputerScience Vol. 4193, Reykjavik, Iceland, September 2006.

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314. P.A. Castillo, M.G. Arenas, J.J. Merelo, V.M. Rivas and G. Romero, “Mul-tiobjective Optimization of Ensembles of Multilayer Perceptrons for Pat-tern Classification”, in Thomas Philip Runarsson, Hans-Georg Beyer, Ed-mund Burke, Juan J. Merelo-Guervos, L. Darrell Whitley and Xin Yao (ed-itors), Parallel Problem Solving from Nature - PPSN IX, 9th InternationalConference, pp. 453–462, Springer. Lecture Notes in Computer ScienceVol. 4193, Reykjavik, Iceland, September 2006.

315. Mike Preuss, Boris Naujoks and Gunter Rudolph, “Pareto Set and EMOABehavior for Simple Multimodal Multiobjective Functions”, in ThomasPhilip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J. Merelo-Guervos,L. Darrell Whitley and Xin Yao (editors), Parallel Problem Solving fromNature - PPSN IX, 9th International Conference, pp. 513–522, Springer.Lecture Notes in Computer Science Vol. 4193, Reykjavik, Iceland, Septem-ber 2006.


317. Min Liu and Dan M. Frangopol, “Optimizing bridge network maintenancemanagement under uncertainty with conflicting criteria: Life-cycle main-tenance, failure, and user costs”, Journal of Structural Engineering–ASCE,Vol. 132, No. 11, pp. 1835–1845, November 2006.

318. Fabio Freschi and Maurizio Repetto, “VIS: an artificial immune networkfor multi-objective optimization”, Engineering Optimization, Vol. 38, No.8, pp. 975–996, December 2006.

319. B. Qian, L. Wang, D.X. Huang and X. Wang, “Multi-objective flow shopscheduling using differential evolution”, Intelligent Computing in SignalProcessing and Pattern Recognition, Springer-Verlag, pp. 1125–1136, Lec-ture Notes in Control and Information Sciences Vol. 345, 2006.

320. F. Luna, A.J. Nebro and E. Alba, “Observations in using Grid-enabled tech-nologies for solving multi-objective optimization problems”, Parallel Com-puting, Vol. 32, Nos. 5-6, pp. 377–393, June 2006.

321. E. Nobile, F. Pinto and G. Rizzetto, “Geometric parameterization and mul-tiobjective shape optimization of convective periodic channels”, NumericalHeat Transfer Part B–Fundamentals, Vol. 50, No. 5, pp. 425–453, Novem-ber 2006.

322. J.G. Villegas, F. Palacios and A.L. Medaglia, “Solution methods for the bi-objective (cost-coverage) unconstrained facility location problem with anillustrative example”, Annals of Operations Research, Vol. 147, No. 1, pp.109–141, October 2006.

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323. D.T. Pham and M. Castellani, “Evolutionary learning of fuzzy models”,Engineering Applications of Artificial Intelligence, Vol. 19, No. 6, pp.583–592, September 2006.

324. K.C. Tan, Y.J. Yang and C.K. Goh, “A Distributed Cooperative Coevolu-tionary Algorithm for Multiobjective Optimization”, IEEE Transactions onEvolutionary Computation, Vol. 10, No. 5, pp. 527–549, October 2006.

325. J.L. Bernal-Agustin, R. Dufo-Lopez and D.M. Rivas-Ascaso, “Design ofisolated hybrid systems minimizing costs and pollutant emissions”, Renew-able Energy, Vol. 31, No. 14, pp. 2227–2244, November 2006.

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327. F. Berlanga, M.J. del Jesus, P. Gonzalez, F. Herrera and M. Mesonero,“Multiobjective evolutionary induction of subgroup discovery fuzzy rules:A case study in marketing”, Advances in Data Mining, pp. 337–349,Springer-Verlag, Lecture Notes in Artificial Intelligence Vol. 4065, 2006.

328. A.S. Kurup, K. Hidajat and A.K. Ray, “Comparative study of modifiedsimulated moving bed systems at optimal conditions for the separation ofternary mixtures of xylene isomers”, Industrial & Engineering ChemistryResearch, Vol. 45, No. 18, pp. 6251–6265, August 30, 2006.

329. T. Biondi, A. Ciccazzo, V. Cutello, S. D’Antona, G. Nicosia and S. Spinella,“Multi-objective evolutionary algorithms and pattern search methods forcircuit design problems”, Journal of Universal Computer Science, Vol. 12,No. 4, pp. 432–449, 2006.

330. R. Kumar and N. Banerjee, “Analysis of a Multiobjective Evolutionary Al-gorithm on the 0-1 knapsack problem”, Theoretical Computer Science, Vol.358, No. 1, pp. 104–120, July 31, 2006.

331. Y. Tang, P. Reed and T. Wagener, “How effective and efficient are multiob-jective evolutionary algorithms at hydrologic model calibration?”, Hydrol-ogy and Earth System Sciences, Vol. 10, No. 2, pp. 289–307, 2006.

332. J.B. Kollat and P.M. Reed, “Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design”, Ad-vances in Water Resources, Vol. 29, No. 6, pp. 792–807, June 2006.

333. B.M. Hodge, F. Pettersson and N. Chakraborti, “Re-evaluation of the op-timal operating conditions for the primary end of an integrated steel plantusing multi-objective genetic algorithms and Nash equilibrium”, Steel Re-search International, Vol. 77, No. 7, pp. 459–461, July 2006.

334. P. Nikitas, A. Pappa-Louisi and P. Agrafiotou, “Multilinear gradient elutionoptimisation in reversed-phase liquid chromatography using genetic algo-rithms”, Journal of Chromatography A, Vol. 1120, Nos. 1–2, pp. 299–307,July 7, 2006.

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335. L. Siwik and M. Kisiel-Dorohinicki, “Semi-elitist evolutionary multi-agentsystem for multiobjective optimization”, Computational Science – ICCS2006, Pt 3, Proceedings, pp. 831–838, Springer-Verlag, Lecture Notes inComputer Science Vol. 3993, 2006.

336. J. Balicki, “Negative selection with ranking procedure in tabu-based multi-criterion evolutionary algorithm for task assignment”, Computational Sci-ence - ICCS 2006, Pt 3, Proceedings, pp. 863–870, Springer-Verlag, Lec-ture Notes in Computer Science Vol. 3993, 2006.

337. N. Nariman-Zadeh, A. Darvizeh and A. Jamali, “Pareto optimization of en-ergy absorption of square aluminium columns using multi-objective geneticalgorithms”, Proceedings of the Institution of Mechanical Engineers PartB–Journal of Engineering Manufacture, Vol. 220, No. 2, pp. 213–224,February 2006.

338. P. Lacomme, C. Prins and M. Sevaux, “A genetic algorithm for a bi-objectivecapacitated arc routing problem”, Computers & Operations Research, Vol.33, No. 12, pp. 3473–3493, December 2006.

339. H.S. Kim and P.N. Roschke, “Fuzzy control of base-isolation system usingmulti-objective genetic algorithm”, Computer-Aided Civil and Infrastruc-ture Engineering, Vol. 21, No. 6, pp. 436–449, August 2006.

340. R. Romero-Zaliz, C. Rubio-Escudero, O. Cordon, O. Harari, C. del Val andI. Zwir, “Mining structural databases: An evolutionary multi-objetive con-ceptual clustering methodology”, in Applications of Evolutionary Comput-ing, pp. 159–171, Springer, Lecture Notes in Computer Science Vol. 3907,2006.

341. Cagkan Erbas, Selin Cerac-Erbas and Andy D. Pimentel, “MultiobjectiveOptimization and Evolutionary Algorithms for the Application MappingProblem in Multiprocessor System-on-Chip Design”, IEEE Transactionson Evolutionary Computation, Vol. 10, No. 3, pp. 358–374, June 2006.

Capıtulos de Libros• Carlos A. Coello Coello, “20 Years of Evolutionary Multi-Objective Optimiza-

tion: What Has Been Done and What Remains to be Done”, in Gary Y. Yen andDavid B. Fogel (editors), Computational Intelligence: Principles and Practice,Chapter 4, pp. 73–88, IEEE Computational Intelligence Society, 2006, ISBN0-9787135-0-8.

1. Xiufen Zou, Yu Chen, Minzhong Liu and Lishan Kang, “A New Evolution-ary Algorithm for Solving Many-Objective Optimization Problems”, IEEETransactions on Systems, Man, and Cybernetics–Part B: Cybernetics, Vol.38, No. 5, pp. 1402–1412, October 2008

• Ricardo Landa Becerra and Carlos A. Coello Coello, “A Cultural Algo-rithm for Solving the Job-Shop Scheduling Problem”, en Yaochu Jin (ed-itor) Knowledge Incorporation in Evolutionary Computation, Springer, pp.

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37–55, Studies in Fuzziness and Soft Computing, Vol. 167, ISBN 3-540-22902-7, 2005.

1. Jesus Garcia, Antonio Berlanga and Jose M. Molina, “Evolutionary algo-rithms in multiply-specified engineering. The MOEAs and WCES strate-gies”, Advanced Engineering Informatics, Vol. 21, No. 1, pp. 3–21, Jan-uary 2007.

• Coello Coello, Carlos A. “Evolutionary Multi-Objective Optimization: ACritical Review”, en Ruhul Sarker, Masoud Mohammadian and Xin Yao(Editores), Evolutionary Optimization, Chapter 5, pp. 117–146, Kluwer Aca-demic Publishers, Boston, ISBN 0-7923-7654-4, February 2002.

1. Jae-Yon Jung and James A. Reggia, “A Descriptive Encoding Languagefor Evolving Modular Neural Networks”, in Kalyanmoy Deb et al. (ed-itors), Genetic and Evolutionary Computation–GECCO 2004. Proceed-ings of the Genetic and Evolutionary Computation Conference. Part II,Springer-Verlag, Lecture Notes in Computer Science Vol. 3103, pp. 519–530, Seattle, Washington, USA, June 2004.

• Coello Coello, Carlos A., “Recent Trends in Evolutionary Multiobjective Opti-mization”, in Ajith Abraham, Lakhmi Jain and Robert Goldberg (editors), Evo-lutionary Multiobjective Optimization: Theoretical Advances And Applications,pp. 7–32, Springer-Verlag, London, 2005, ISBN 1-85233-787-7.


2. Ricardo Perera and Antonio Ruiz, “A multistage FE updating procedure fordamage identification in large-scale structures based on multiobjective evo-lutionary optimization”, Mechanical Systems and Signal Processing, Vol.22, No. 4, pp. 970–991, May 2008.

3. Ricardo Perera, Antonio Ruiz and Carlos Manzano, “An evolutionary mul-tiobjective framework for structural damage localization and quantifica-tion”, Engineering Structures, Vol. 29, No. 10, pp. 2540–2550, October2007.

• Coello Coello, Carlos A., Toscano Pulido, Gregorio and Mezura Montes, Efren,“Current and Future Research Trends in Evolutionary Multiobjective Optimiza-tion”, in Manuel Grana, Richard Duro, Alicia d’Anjou, and Paul P. Wang (ed-itors), Information Processing with Evolutionary Algorithms: From IndustrialApplications to Academic Speculations, pp. 213–231, Springer-Verlag, ISBN1-8523-3866-0, 2005.

1. J.R. Jimenez-Octavio, O. Lopez-Garcia, E. Pilot and A. Carnicero, “Cou-pled electromechanical optimization of power transmission”, CMES-ComputerModeling in Engineering & Sciences, Vol. 25, No. 2, pp. 81–97, February2008.

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2. J.M. Herrero, X. Blasco, M. Martinez, C. Ramos and J. Sanchis, “Non-linear robust identification of a greenhouse model using multi-objectiveevolutionary algorithms”, Biosystems Engineering, Vol. 98, No. 3, pp.335–346, 2007.

3. Daniel E. Salazar and Claudio M. Rocco, “Solving advanced multi-objectiverobust designs by means of multiple objective evolutionary algorithms (MOEA):A reliability application”, Reliability Engineering & System Safety, Vol.92, No. 6, pp. 697–706, June 2007.

4. Diego Sal and Manuel Grana, “Hyperspectral image watermarking withan evolutionary algorithm”, Knowledge-Based Intelligent Information andEngineering Systems, Pt 1, Proceedings, pp. 833–839, Springer, LectureNotes in Artificial Intelligence Vol. 3681, 2005.

• Carlos A. Coello Coello, “Evolutionary Multiobjective Optimization: Currentand Future Challenges”, en Jose Benitez, Oscar Cordon, Frank Hoffmann andRajkumar Roy (editors), Advances in Soft Computing—Engineering, Design andManufacturing, pp. 243–256, Springer-Verlag, ISBN 1-85233-755-9, September2003.



3. Wangshu Yao, Chen Shifu and Chen Zhaoqian, “SDMOGA: A New Multi-objective Genetic Algorithm Based on Objective Space Divided”, in IrwinKing, Jun Wang, Laiwan Chan and DeLiang L. Wang (editors), Neural In-formation Processing, 13th International Conference, ICONIP 2006, PartIII, pp. 754–762, Springer-Verlag. Lecture Notes in Computer Science Vol.4234, Hong Kong, China, October 2006.


• Carlos A. Coello Coello and Carlos E. Mariano Romero, “Evolutionary Al-gorithms and Multiple Objective Optimization”, en Xavier Gandibleux &Matthias Ehrgott (editors), Multiple Criteria Optimization. State of the ArtAnnotated Bibliographic Survey, Chapter 6, pp. 277-331, Kluwer’s Interna-tional Series in Operations Research and Management Science, Volume 52,Kluwer Academic Publishers, ISBN 1-4020-7128-0, June 2002.

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1. Samya Elaoud, Jacques Teghem and Bassem Bouaziz, “Genetic algorithmsto solve the cover printing problem”, Computers & Operations Research,Vol. 34, No. 11, pp. 3346–3361, November 2007.

2. Samya Elaoud, Taicir Loukil and Jacques Teghem, “The Pareto fitness ge-netic algorithm: Test function study”, European Journal of OperationalResearch, Vol. 177, No. 3, pp. 1703–1719, March 16, 2007.

Journals Internacionales• Carlos A. Coello Coello and Nareli Cruz Cortes, “Hybridizing a Genetic Algo-

rithm with an Artificial Immune System for Global Optimization”, EngineeringOptimization, Vol. 36, No. 5, pp. 607–634, October 2004.

1. Ali R. Yildiz, Nursel Ozturk, Necmettin Kaya and Ferruh Ozturk, “Hybridmulti-objective shape design optimization using Taguchi’s method and ge-netic algorithm”, Structural and Multidisciplinary Optimization, Vol. 34,No. 4, pp. 317–332, October 2007.

2. George G. Dimopoulos, “Mixed-variable engineering optimization basedon evolutionary and social metaphors”, Computer Methods in Applied Me-chanics and Engineering, Vol. 196, Nos. 4–6, pp. 803–817, 2007.

3. Rein Luus, Kelly Sabaliauskas and Ihor Harapyn, “Handling inequalityconstraints in direct search optimization”, Engineering Optimization, Vol.38, No. 4, pp. 391–405, June 2006.

4. P. Musilek, A. Lau, M. Reformat and L. Wyard-Scott, “Immune program-ming”, Information Sciences, Vol. 176, No. 8, pp. 972–1002, April 22,2006.

• Efren Mezura Montes and Carlos A. Coello Coello, “A Simple Multi-MemberedEvolution Strategy to Solve Constrained Optimization Problems”, IEEE Trans-actions on Evolutionary Computation, Vol. 9, No. 1, pp. 1–17, February 2005.

1. Yong Wang, Zixing Cai, Yuren Zhou and Zhun Fan, “Constrained opti-mization based on hybrid evolutionary algorithm and adaptive constraint-handling technique”, Structural and Multidisciplinary Optimization, Vol.37, No. 4, pp. 395–413, January 2009.

2. Sushil Kumar and R. Naresh, “Efficient real coded genetic algorithm tosolve the non-convex hydrothermal scheduling problem”, International Jour-nal of Electrical Power & Energy Systems, Vol. 29, No. 10, pp. 738–747,December 2007.

3. Ehab Z. Elfeky, Ruhul A. Sarker and Daryl L. Essam, “Analyzing the sim-ple ranking and selection process for constrained evolutionary optimiza-tion”, Journal of Computer Science and Technology, Vol. 23, No. 1, pp.19–34, January 2008.

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5. Yong Wang, Zixing Cai, Yuren Zhou and Wei Zeng, “An Adaptive TradeoffModel for Constrained Evolutionary Optimization”, IEEE Transactions onEvolutionary Computation, Vol. 12, No. 1, pp. 80–92, February 2008.

6. J. von Berg and C. Lorenz, “A geometric model of the beating heart”, Meth-ods of Information in Medicine, Vol. 46, No. 3, pp. 282–286, 2007.

7. Jing Liu and Weicai Zhong, “Constrained Optimization Using Organiza-tional Evolutionary Algorithm”, in Tzai-Der Wang, Xiaodong Li, Shu-Heng Chen, Xufa Wang, Hussein Abbass, Hitoshi Iba, Guoliang Chen andXin Yao (editors), Simulated Evolution and Learning, 6th InternationalConference, SEAL 2006, pp. 302–309, Springer. Lecture Notes in Com-puter Science Vol. 4247, Hefei, China, October 2006.

8. Elizabeth F. Wanner, Ricardo H.C. Takahashi, Frederico G. Guimaraesand Jaime A. Ramirez, “Hybrid genetic algorithms using quadratic localsearch operators”, COMPEL-The International Journal for Computationand Mathematics in Electrical and Electronic Engineering, Vol. 26, No. 3,pp. 773–787, 2007.


10. Yong Wang, Hui Liu, Zixing Cai and Yuren Zhou, “An orthogonal designbased constrained evolutionary optimization algorithm”, Engineering Op-timization, Vol. 39, No. 6, pp. 715–736, September 2007.

11. Pei Yee Ho and Kazuyuki Shimizu, “Evolutionary constrained optimiza-tion using an addition of ranking method and a percentage-based tolerancevalue adjustment scheme”, Information Sciences, Vol. 177, No. 14, pp.2985–3004, July 15, 2007.

12. Yong Wang, Zixing Cai, Guanqi Guo and Yuren Zhou, “Multiobjectiveoptimization and hybrid evolutionary algorithm to solve constrained opti-mization problems”, IEEE Transactions on Systems, Man and CyberneticsPart B–Cybernetics, Vol. 37, No. 3, pp. 560–575, June 2007.

13. Fu-zhuo Huang, Ling Wang and Qie He, “An effective co-evolutionary dif-ferential evolution for constrained optimization”, Applied Mathematics andComputation, Vol. 186, No. 1, pp. 340–356, March 1, 2007.

14. Felipe Campelo, So Noguchi and Hajime Igarashi, “A new method for therobust design of high field, highly homogenous superconducting magnetsusing an immune algorithm”, IEEE Transactions on Applied Applied Su-perconductivity, Vol. 16, No. 2, pp. 1316–1319, June 2006.

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15. Yuanpng Guo, Xianbin Cao, Hongzhang Yin and Zeying Tang, “Coevolu-tionary optimization algorithm with dynamic sub-population size”, Inter-national Journal of Innovative Computing Information and Control, Vol.2, No. 2, pp. 435–448, April 2007.

16. Yiqing Luo, Xigang Yuan and Yongjian Liu, “An improved PSO algorithmfor solving non-convex NLP/MINLP problems with equality constraints”,Computers & Chemical Engineering, Vol. 31, No. 3, pp. 153–162, January29, 2007.

17. Ehab Z. Elfeky, Ruhul A. Sarker and Daryl L. Essam, “A simple rank-ing and selection for constrained evolutionary optimization”, in Tzai-DerWang, Xiaodong Li, Shu-Heng Chen, Xufa Wang, Hussein Abbass, HitoshiIba, Guoliang Chen and Xin Yao (editors), Simulated Evolution and Learn-ing, 6th International Conference, SEAL 2006, pp. 537–544, Springer.Lecture Notes in Computer Science Vol. 4247, Hefei, China, October 2006.

18. A.R. Yildiz and F. Ozturk, “Hybrid enhanced genetic algorithm to selectoptimal machining parameters in turning operation”, Proceedings of theInstitution of Mechanical Engineers Part B–Journal of Engineering Manu-facture, Vol. 220, No. 12, pp. 2041–2053, December 2006.

19. Qie He and Ling Wang, “An effective co-evolutionary particle swarm op-timization for constrained engineering design problems”, Engineering Ap-plications of Artificial Intelligence, Vol. 20, No. 1, pp. 89–99, February2007.

20. Zixing Cai and Yong Wang, “A Multiobjective Optimization-Based Evo-lutionary Algorithm for Constrained Optimization”, IEEE Transactions onEvolutionary Computation, Vol. 10, No. 6, pp. 658–675, December 2006.

21. Philip Hingston, Luigi Barone, Simon Huband and Lyndon While, “Multi-level Ranking for Constrained Multi-objective Evolutionary Optimisation”,in Thomas Philip Runarsson, Hans-Georg Beyer, Edmund Burke, Juan J.Merelo-Guervos, L. Darrell Whitley and Xin Yao (editors), Parallel Prob-lem Solving from Nature - PPSN IX, 9th International Conference, pp. 563–572, Springer. Lecture Notes in Computer Science Vol. 4193, Reykjavik,Iceland, September 2006.

22. A.R. Hedar and M. Fukushima, “Derivative-free filter simulated annealingmethod for constrained continuous global optimization”, Journal of GlobalOptimization, Vol. 35, No. 4, pp. 521–549, August 2006.

23. Felipe Campelo, Frederico G. Guimaraes, Hajime Igarashi, Jaime A. Ramirezand So Noguchi, “A modified immune network algorithm for multimodalelectromagnetic problems”, IEEE Transactions on Magnetics, Vol. 42, No.4, pp. 1111–1114, April 2006.

24. Hong Li, Yong-Chang Jiao and Yuping Wang, “Integrating the Simpli-fied Interpolation into the Genetic Algorithm for Constrained OptimizationProblems”, in Yue Hao et al. (editors), Computational Intelligence and

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25. Yuping Wang, Dalian Liu, and Yiu-Ming Cheung, “Preference Bi-objectiveEvolutionary Algorithm for Constrained Optimization”, in Yue Hao et al.(editors), Computational Intelligence and Security. International Confer-ence, CIS 2005, pp. 184–191, Springer, Lecture Notes in Artificial Intelli-gence Vol. 3801, Xi’an, China, December 2005.

• Carlos A. Coello Coello, Gregorio Toscano Pulido and Maximino SalazarLechuga, “Handling Multiple Objectives with Particle Swarm Optimiza-tion”, IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, pp.256–279, June 2004.

1. Hongwu Liu and Ji Li, “A particle swarm optimization-based multiuser de-tection for receive-diversity-aided STBC systems”, IEEE Signal Process-ing Letters, Vol. 15, pp. 29–32, 2008.


3. Ching-Shih Tsou, “Multi-objective inventory planning using MOPSO andTOPSIS”, Expert Systems with Applications, Vol. 35, Nos. 1–2, pp. 136–142, July-August 2008.

4. Shubham Agrawal, B.K. Panigrahi and Manoj Kumar Tiwari, “Multiobjec-tive Particle Swarm Algorithm with Fuzzy Clustering for Electrical PowerDispatch”, IEEE Transactions on Evolutionary Computation, Vol. 12, No.5, pp. 529–541, October 2008.

5. Kazuhiro Izui, Shinji Nishiwaki, Masataka Yoshimura, Masahiko Naka-mura and John E. Renaud, “Enhanced multiobjective particle swarm op-timization in combination with adaptive weighted gradient-based search-ing”, Engineering Optimization, Vol. 40, No. 9, pp. 789–804, September2008.


7. Antonio J. Nebro, Francisco Luna, Enrique Alba, Bernabe Dorronsoro,Juan J. Durillo and Andreas Beham, “AbYSS: Adapting Scatter Search toMultiobjective Optimization”, IEEE Transactions on Evolutionary Com-putation, Vol. 12, No. 4, pp. 439–457, August 2008.

8. Min-Rong Chen and Yong-Zal Lu, “A novel elitist multiobjective optimiza-tion algorithm: Multiobjective extremal optimization”, European Journalof Operational Research, Vol. 188, No. 3, pp. 637–651, August 1, 2008.

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9. Yifeng Niu, Lincheng Shen and Yanlong Bu, “Multi-objective blind im-age fusion”, in Rough Sets and Knowledge Technology, Springer. LectureNotes in Artificial Intelligence Vol. 4062, pp. 713–720, 2006.

10. Hamidreza Eskandari and Christopher D. Geiger, “A fast Pareto genetic al-gorithm approach for solving expensive multiobjective optimization prob-lems”, Journal of Heuristics, Vol. 14, No. 3, pp. 203–241, June 2008.

11. Yamille del Valle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi,Jean-Carlos Hernandez and Ronald G. Harley, “Particle Swarm Optimiza-tion: Basic Concepts, Variants and Applications in Power Systems”, IEEETransactions on Evolutionary Computation, Vol. 12, No. 2, pp. 171–195,April 2008.

12. Shubham Agrawal, Yogesh Dashora, Manoj Kumar Tiwari and Young-JunSon, “Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Mul-tiobjective Optimization”, IEEE Transactions on Systems, Man, and Cyber-netics Part A–Systems and Humans, Vol. 38, No. 2, pp. 258–277, March2008.

13. Yifeng Niu and Lincheng Shen, “An Adaptive Multi-objective Particle SwarmOptimization for Color Image Fusion”, in Tzai-Der Wang, Xiaodong Li,Shu-Heng Chen, Xufa Wang, Hussein Abbass, Hitoshi Iba, Guoliang Chenand Xin Yao (editors), Simulated Evolution and Learning, 6th InternationalConference, SEAL 2006, pp. 473–480, Springer. Lecture Notes in Com-puter Science Vol. 4247, Hefei, China, October 2006.

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