citations to publications of dr. carlos a. coello coello total:...

Citations to publications of

Dr. Carlos A. Coello Coello

Total: 11,057 (excluding self-citations and citations from his

co-authors)

Libros

• Carlos A. Coello Coello, Gary B. Lamont and David A. Van Veldhuizen, “Evolutionary Algorithms forSolving Multi-Objective Problems”, Second Edition, Springer-Verlag, New York, USA, September 2007,ISBN 978-0-387-33254-3.1

1. Leonardo C.T. Bezerra,Manuel López-Ibañez and Thomas Stützle, “Automatic Component-Wise Design of Multiobjec-tive Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 3, pp. 403–417, June2016.

2. Murilo Zangari, Alexander Mendiburu, Roberto Santana and Aurora Pozo, “Multiobjective decomposition-based MallowsModels estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem”, InformationSciences, Vol. 397, pp. 137–154, August 2017.

3. Yanan Sun, Gary G. Yen and Zhang Yi, “Reference line-based Estimation of Distribution Algorithm for many-objectiveoptimization”, Knowledge-Based Systems, Vol. 132, pp. 129–143, September 15, 2017.

4. Masoud Afrand, Said Farahat, Alireza Hossein Nezhad, Ghanbar Ali Sheikhzadeh, Faramarz Sarhaddi and SomchaiWongwises, “Multi-objective optimization of natural convection in a cylindrical annulus mold under magnetic field usingparticle swarm algorithm”, International Communications in Heat and Mass Transfer, Vol. 60, pp. 13–20, January2015.

5. Xinye Cai, Zhixiang Yang, Zhun Fan and Qingfu Zhang, “Decomposition-Based-Sorting and Angle-Based-Selection forEvolutionary Multiobjective and Many-Objective Optimization”, IEEE Transactions on Cybernetics, Vol. 47, No. 9,pp. 2824–2837, September 2017.

6. Luis Lobato Macedo, Pedro Godinho and Maria Joao Alves, “Mean-semivariance portfolio optimization with multiobjec-tive evolutionary algorithms and technical analysis rules”, Expert Systems with Applications, Vol. 79, pp. 33–43, August15, 2017.

7. Helio Freire, P.B. Moura Oliveira and E.J. Solteiro Pires, “From Single to Many-objective PID Controller Design usingParticle Swarm Optimization”, International Journal of Control Automation and Systems, Vol. 15, No. 2, pp. 918–932,April 2017.

8. Aurora Ramirez, Jose Antonio Parejo, Jose Raul Romero, Sergio Segura and Antonio Ruiz-Cortes, “Evolutionary com-position of QoS-aware web services: A many-objective perspective”, Expert Systems with Applications, Vol. 72, pp.357–370, April 15, 2017.

9. Laura Cruz-Reyes, Eduardo Fernandez and Nelson Rangel-Valdez, “A metaheuristic optimization-based indirect elicita-tion of preference parameters for solving many-objective problems”, International Journal of Computational IntelligenceSystems, Vol. 10, No. 1, pp. 56–77, January 2017.

10. Yi Xiang, Yuren Zhou, Miqing Li and Zefeng Chen, “A Vector Angle-Based Evolutionary Algorithm for UnconstrainedMany-Objective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 21, No. 1, pp. 131–152, Febru-ary 2017.

11. Eduardo Segredo, Carlos Segura and Coromoto Leon, “Memetic algorithms and hyperheuristics applied to a multi-objectivised two-dimensional packing problem”, Journal of Global Optimization, Vol. 58, No. 4, pp. 769–794, April2014.

12. Antonio Gaspar-Cunha, Jose Ferreira and Gustavo Recio, “Evolutionary robustness analysis for multi-objective opti-mization: benchmark problems”, Structural and Multidisciplinary Optimization, Vol. 49, No. 5, pp. 771–793, May2014.

1También se incluyen aqúı las citas a la primera edición:Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont, “Evolutionary Algorithms for Solving Multi-Objective Problems”, KluwerAcademic Publishers, New York, USA, ISBN 0-3064-6762-3, May 2002.

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13. Yongpeng Shen and Yaonan Wang, “Operating Point Optimization of Auxiliary Power Unit Using Adaptive Multi-Objective Differential Evolution Algorithm”, IEEE Transactions on Industrial Electronics, Vol. 64, No. 1, pp. 115–124,January 2017.

14. Yi-xin Su and Rui Chi, “Multi-objective particle swarm-differential evolution algorithm”, Neural Computing & Applica-tions, Vol. 28, No. 2, pp. 407–418, February 2017.

15. Justin J. Kelly and Christian Jacob, “evoVision3D: A Multiscale Visualization of Evolutionary Histories”, in Julia Handl,Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa and Ben Paechter (Editors), Parallel Problem Solvingfrom Nature – PPSN XIV, 14th International Conference, pp. 942–951, Lecture Notes in Computer Science Vol. 9921,Edinburgh, UK, September 17-21, 2016, ISBN 978-3-319-45822-9.

16. T. Lust and D. Tuyttens, “Variable and large neighborhood search to solve the multiobjective set covering problem”,Journal of Heuristics, Vol. 20, No. 2, pp. 165–188, April 2014.

17. Cristobal J. Carmona, Pedro Gonzalez, Maria Jose del Jesus and Francisco Herrera, “Overview on evolutionary sub-group discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms”, WileyInterdisciplinary Reviews–Data Mining and Knowledge Discovery, Vol. 4, No. 2, pp. 87–103, March 2014.

18. Z.M. Nopiah, M.H. Osman, S. Abdullah and M.N. Baharin, “Application of a Multi-Objective Approach and SequentialCovering Algorithm to the Fatigue Segment Classification Problem”, Arabian Journal for Science and Engineering, Vol.39, No. 3, pp. 2165–2177, March 2014.

19. Arnaud Liefooghe, Sebastien Verel and Jin-Kao Hao, “A hybrid metaheuristic for multiobjective unconstrained binaryquadratic programming”, Applied Soft Computing, Vol. 16, pp. 10–19, March 2014.

20. Stefanos V. Papaefthymiou and Stavros A. Papathanassiou, “Optimum sizing of wind-pumped-storage hybrid powerstations in island systems”, Renewable Energy, Vol. 64, pp. 187–196, April 2014.

21. Weihua Zhang and Marc Reimann, “A simple augmented epsilon-constraint method for multi-objective mathematicalinteger programming problems”, European Journal of Operational Research, Vol. 234, No. 1, pp. 15–24, April 1, 2014.

22. Yu Lei, Maoguo Gong, Jun Zhang, Wei Li and Licheng Jiao, “Resource allocation model and double-sphere crowdingdistance for evolutionary multi-objective optimization”, European Journal of Operational Research, Vol. 234, No. 1, pp.197–208, April 1, 2014.

23. E.J. Solteiro Pires, J.A. Tenreiro Machado, Antonio M. Lopes and P.B. de Moura Oliveira, “Optimal Location of theWorkpiece in a PKM-Based Machining Robotic Cell”, in G. Fornarelli and K. Mescia (Editors), Swarm Intelligence forElectric and Electronic Engineering, pp. 223–236, IGI Global, Hershey, Philadelphia, USA, 2013, ISBN 978-1-4666-2697-3.

24. Fernando Jimenez, Gracia Sanchez and Jose M. Juarez, “Multi-objective evolutionary algorithms for fuzzy classificationin survival prediction”, Artificial Intelligence in Medicine, Vol. 60, No. 3, pp. 197–219, March 2014.

25. Rui Wang, Peter J. Fleming and Robin C. Purshouse, “General framework for localised multi-objective evolutionaryalgorithms”, Information Sciences, Vol. 258, pp. 29–53, February 10, 2014.

26. Cai Dai and Yuping Wang, “A New Multiobjective Evolutionary Algorithm Based on Decomposition of the ObjectiveSpace for Multiobjective Optimization”, Journal of Applied Mathematics, Article Number: 906147, 2014.

27. Wei-Yu Chiu, Gary G. Yen and Teng-Kuei Juan, “Minimum Manhattan Distance Approach to Multiple Criteria DecisionMaking in Multiobjective Optimization Problems”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 6,pp. 972–985, December 2016.

28. Patricia Melin, “Genetic Optimization of Modular Neural Networks for Pattern Recognition with a Granular Approach”,in Plamen Parvanov Angelov (editor), Handbook on Computational Intelligence, Volume 2: Evolutionary Computation,Hybrid Systems, and Applications, Chapter 21, pp. 731–744, World Scientific, Singapore, 2016, ISBN 978-981-4675-04-8.

29. Michela Antonelli, Pietro Ducange and Francesco Marcelloni, “Multi-Objective Evolutionary Design of Fuzzy Rule-BasedSystems”, in Plamen Parvanov Angelov (editor), Handbook on Computational Intelligence, Volume 2: EvolutionaryComputation, Hybrid Systems, and Applications, Chapter 18, pp. 635–670, World Scientific, Singapore, 2016, ISBN978-981-4675-04-8.

30. Dipesh Pradhan, Shuai Wang, Shaukat Ali and Tao Yue, “Search-Based Cost-Effective Test Case Selection Within aTime Budget: An Empirical Study”, in 2016 Genetic and Evolutionary Computation Conference (GECCO’2016), pp.1085–1092, ACM Press, Denver, Colorado, USA, 20-24 July, 2016, ISBN 978-1-4503-4206-3.

31. Justin J. Kelly and Christian Jacob, “evoVision3D: A Multiscale Visualization of Evolutionary Histories”, in Julia Handl,Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, Gabriela Ochoa and Ben Paechter (Editors), Parallel Problem Solvingfrom Nature – PPSN XIV, 14th International Conference, pp. 942–951, Springer. Lecture Notes in Computer ScienceVol. 9921, Edinburgh, UK, September 17-21, 2016, ISBN 978-3-319-45822-9.

32. Shana Schlottfeldt, Maria Emilia M.T. Walter, Jon Timmis, André C.P.L.F. Carvalho, Mariana P.C. Telles and JoséAlexandre F. Diniz-Filho, “Using Multi-Objective Artificial Immune Systems to Find Core Collections Based on Molec-ular Markers”, in 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 1271–1278, ACM Press,Madrid, Spain, July 11-15, 2015, ISBN 978-1-4503-3472-3.

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33. Bin Xu, Rongbin Qi, Weimin Zhong, Wenli Du and Feng Qian, “Optimization of p-xylene oxidation reaction processbased on self-adaptive multi-objective differential evolution”, Chemometrics and Intelligent Laboratory Systems, Vol.127, pp. 55–62, August 15, 2013.

34. Kleopatra Pirpinia, Tanja Alderliesten, Jan-Jakob Sonke, Marcel van Herk and Peter A.N. Bosman, “Diversifying Multi-Objective Gradient Techniques and Their Role in Hybrid Multi-Objective Evolutionary Algorithms for DeformableMedical Image Registration”, in 2015 Genetic and Evolutionary Computation Conference (GECCO 2015), pp. 1255–1262, ACM Press, Madrid, Spain, July 11-15, 2015, ISBN 978-1-4503-3472-3.

35. Rui Wang, Qingfu Zhang and Tao Zhang, “Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Meth-ods”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 6, pp. 821–837, December 2016.

36. Parviz Fattahi, Vahid Hajipour and Arash Nobari, “A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms”, Applied Soft Computing, Vol. 32, pp. 211–223, July 2015.

37. Manuel Barraza, Eden Bojorquez, Eduardo Fernandez-Gonzalez and Alfredo Reyes-Salazar, “Multi-objective Optimiza-tion of Structural Steel Buildings under Earthquake Loads using NSGA-II and PSO”, KSCE Journal of Civil Engineering,Vol. 21, No. 2, pp. 488–500, February 2017.

38. Abraham Duarte, Juan J. Pantrigo, Edugardo G. Pardo and Nenad Mladenovic, “Multi-objective variable neighborhoodsearch: an application to combinatorial optimization problems”, Journal of Global Optimization, Vol. 63, No. 3, pp.515–536, November 2015.

39. Pezhman Sharafi, Lip H. Teh and Muhammad N.S. Hadi, “Conceptual design optimization of rectilinear building frames:A knapsack problem approach”, Engineering Optimization, Vol. 47, No. 10, pp. 1303–1323, October 3, 2015.

40. Murat Köksalan, Jyrki Wallenius and Stanley Zionts, “Multiple Criteria Decision Making. From Early History to the21st Century”, World Scientific, Singapore, 2011, ISBN 981-4335-58-4.

41. Hu Zhang, Shenmin Song, Aimin Zhou and X.Z. Gao, “A multiobjective cellular genetic algorithm based on 3D structureand cosine crowding measurement”, International Journal of Machine Learning and Cybernetics, Vol. 6, No. 3, pp. 487–500, June 2015.

42. Xin Qiu, Jian-Xin Xu, Kay Chen Tan and Hussein A. Abbass, “Adaptive Cross-Generation Differential EvolutionOperators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 2, pp.232–244, April 2016.

43. Sara Perez-Carabaza, Eva Besada-Portas, Jose A. Lopez-Orozco and Jesus M. de la Cruz, “A Real World Multi-UavEvolutionary Planner for Minimum Time Target Detection”, in 2016 Genetic and Evolutionary Computation Conference(GECCO’2016), pp. 981–988, ACM Press, Denver, Colorado, USA, 20-24 July, 2016, ISBN 978-1-4503-4206-3.

44. Darcy Chia and Lyndon While, “Automated Design of Architectural Layouts Using a Multi-Objective EvolutionaryAlgorithm”, in Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, YaochuJin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen Tan and Ke Tang (editors), Simulated Evolution and Learning,10th International Conference, SEAL 2014, pp. 760–772, Springer. Lecture Notes in Computer Science Vol. 8886,Dunedin, New Zealand, December 15-18, 2014.

45. Bing Xue, Wenlong Fu and Mengjie Zhang, “Multi-objective Feature Selection in Classification: A Differential EvolutionApproach”, in Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin,Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen Tan and Ke Tang (editors), Simulated Evolution and Learning, 10thInternational Conference, SEAL 2014, pp. 516–528, Springer. Lecture Notes in Computer Science Vol. 8886, Dunedin,New Zealand, December 15-18, 2014.

46. Frank Neumann and Anh Quang Nguyen, “On the Impact of Utility Functions in Interactive Evolutionary Multi-objectiveOptimization”, in Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, YaochuJin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen Tan and Ke Tang (editors), Simulated Evolution and Learning,10th International Conference, SEAL 2014, pp. 419–430, Springer. Lecture Notes in Computer Science Vol. 8886,Dunedin, New Zealand, December 15-18, 2014.

47. Hiroyuki Sato, “Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization”, inGrant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin, Xiaodong Li,Yuhui Shi, Pramod Singh, Kay Chen Tan and Ke Tang (editors), Simulated Evolution and Learning, 10th InternationalConference, SEAL 2014, pp. 274–286, Springer. Lecture Notes in Computer Science Vol. 8886, Dunedin, New Zealand,December 15-18, 2014.

48. Sandra M. Venske, Richard A. Goncalves, Elaine M. Benelli and Myriam R. Delgado, “ADEMO/D: An adaptive dif-ferential evolution for protein structure prediction problem”, Expert Systems with Applications, Vol. 56, pp. 209–226,September 1, 2016.

49. Hernán Aguirre, Arnaud Liefooghe, Sébastien Verel and Kiyoshi Tanaka, “An Analysis on Selection for High-ResolutionApproximations in Many-Objective Optimization”, in Thomas Bartz-Beielstein, Jürgen Branke, Bogdan Filipic andJim Smith (editors), Parallel Problem Solving from Nature - PPSN XIII, 13th International Conference, pp. 487–497,Springer. Lecture Notes in Computer Science Vol. 8672, Ljubljana, Slovenia, September 13-17, 2014.

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50. Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff and Hisao Ishibuchi, “Preferencerepresentation using Gaussian functions on a hyperplane in evolutionary multi-objective optimization”, Soft Computing,Vol. 20, No. 7, pp. 2733–2757, July 2016.

51. Xiaoliang Ma, Fang Liu, Yutao Qi, Lingling Li, Licheng Jiao, Xiaozheng Deng, Xiaodong Wang, Bei Dong, ZhantingHou, Yongxiao Zhang and Jianshe Wu, “MOEA/D with biased weight adjustment inspired by user preference and itsapplication on multi-objective reservoir flood control problem”, Soft Computing, Vol. 20, No. 12, pp. 4999–5023,December 2016.

52. José Roberto Méndez Rosiles, Antonin Ponsich, Eric Alfredo Rincón Garćıa and Roman Anselmo Mora Gutiérrez,“Extension of the Method of Musical Composition for the Treatment of Multi-objective Optimization Problems”, inA. Gelbukh, F. C. Espinoza and S.N. Galicia-Haro (editors), Nature-inspired Computation and Machine Learning, 13thMexican International Conference on Artificial Intelligence, MICAI 2014, pp. 38–49, Springer. Lecture Notes in ArtificialIntelligence Vol. 8857, Tuxtla Gutierrez, México, November 16-22, 2014.

53. Benjamin Doerr, Wanru Gao and Frank Neumann, “Runtime Analysis of Evolutionary Diversity Maximization forOneMinMax”, in 2016 Genetic and Evolutionary Computation Conference (GECCO’2016), pp. 557–564, ACM Press,Denver, Colorado, USA, 20-24 July, 2016, ISBN 978-1-4503-4206-3.

54. Mohamed Abouhawwash and Kalyanmoy Deb, “Karush-Kuhn-Tucker Proximity Measure for Multi-Objective Optimiza-tion Based on Numerical Gradients”, in 2016 Genetic and Evolutionary Computation Conference (GECCO’2016), pp.525–532, ACM Press, Denver, Colorado, USA, 20-24 July, 2016, ISBN 978-1-4503-4206-3.

55. Edgar Galván-López, Efrén Mezura-Montes, Ouassim Ait ElHara and Marc Schoenauer, “On the Use of Semantics inMulti-Objective Genetic Programming”, in Julia Handl, Emma Hart, Peter R. Lewis, Manuel López-Ibáñez, GabrielaOchoa and Ben Paechter (Editors), Parallel Problem Solving from Nature – PPSN XIV, 14th International Conference,pp. 353–363, Springer. Lecture Notes in Computer Science Vol. 9921, Edinburgh, UK, September 17-21, 2016, ISBN978-3-319-45822-9.

56. Clarisse Dhaenens and Laetitia Jourdan, “Metaheuristics for Big Data”, Wiley, Hoboken, New Jersey, USA, 2016, ISBN978-1-84821-806-2.

57. Rajan Filomeno Coelho, “Bi-objective hypervolume-based Pareto optimization A gradient-based approach as an alter-native to evolutionary algorithms”, Optimization Letters, Vol. 9, No. 6, pp. 1091–1103, August 2015.

58. B. Rosario Campomanes-Alvarez, Oscar Cordon and Sergio Damas, “Evolutionary multi-objective optimization for meshsimplification of 3D open models”, Integrated Computer-Aided Engineering, Vol. 20, No. 4, pp. 375–390, 2013.

59. Oliver Schutze,Victor Adrian Sosa Hernandez, Heike Trautmann and Gunter Rudolph, “The hypervolume based directedsearch method for multi-objective optimization problems”, Journal of Heuristics, Vol. 22, No. 3, pp. 273–300, June2016.

60. Ishibuchi, Yuji Sakane, Noritaka Tsukamoto and Yusuke Nojima, “Evolutionary Many-Objective Optimization byNSGA-II and MOEA/D with Large Populations”, in IEEE International Conference on Systems, Man and Cybernetics(SMC’2009), pp. 1758–1763, IEEE Press, Texas, USA, October 2009, ISBN 978-1-4244-2794-9.

61. Minami Miyakawa, Keiki Takadama and Hiroyuki Sato, “Directed Mating Using Inverted PBI Function for ConstrainedMulti-Objective Optimization”, in 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pp. 2929–2936,IEEE Press, Sendai, Japan, 25-28 May 2015, ISBN 978-1-4799-7492-4.

62. Haitham Seada and Kalyanmoy Deb, “Effect of Selection Operator on NSGA-III in Single, Multi, and Many-ObjectiveOptimization”, in 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pp. 2915–2922, IEEE Press, Sendai,Japan, 25-28 May 2015, ISBN 978-1-4799-7492-4.

63. Fei Li, Jianchang Liu, Shubin Tan and Xia Yu, “R2-MOPSO: A Multi-Objective Particle Swarm Optimizer Based onR2-Indicator and Decomposition”, in 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pp. 3148–3155,IEEE Press, Sendai, Japan, 25-28 May 2015, ISBN 978-1-4799-7492-4.

64. J.M. Luna, J.R. Romero and S. Ventura, “Grammar-based multi-objective algorithms for mining association rules”, Data& Knowledge Engineering, Vol. 86, pp. 19–37, July 2013.

65. Ana Respicio, Margarida Moz and Margarida Vaz Pato, “Enhanced genetic algorithms for a bi-objective bus driverrostering problem: a computational study”, International Transactions in Operational Research, Vol. 20, No. 4, pp.443–470, July 2013.

66. Rodolfo Eleazar Perez Loaiza, Elias Olivares-Benitez, Pablo A. Miranda Gonzalez, Aaron Guerrero Campanur and JoseLuis Martinez Flores, “Supply chain network design with efficiency, location, and inventory policy using a multiobjectiveevolutionary algorithm”, International Transactions in Operational Research, Vol. 24, Nos. 1-2, pp. 251–275, January-March 2017.

67. Sergio Nesmachnow, Cristian Perfumo and Inigo Goiri, “Holistic multiobjective planning of datacenters powered byrenewable energy”, Cluster Computing–Journal of Networks Software Tools and Applications, Vol. 18, No. 4, pp.1379–1397, December 2015.

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68. Santiago Iturriaga, Bernabe Dorronsoro and Sergio Nesmachnow, “Multiobjective evolutionary algorithms for energy andservice level scheduling in a federation of distributed datacenters”, International Transactions in Operational Research,Vol. 24, Nos. 1-2, pp. 199–228, January-March 2017.

69. Alberto Fernandez, Victoria Lopez, Maria Jose del Jesus and Francisco Herrera, “Revisiting Evolutionary Fuzzy Systems:Taxonomy, applications, new trends and challenges”, Knowledge-Based Systems, Vol. 80, pp. 109–121, May 2015.

70. Gift Dumedah, “Toward essential union between evolutionary strategy and data assimilation for model diagnostics:An application for reducing the search space of optimization problems using hydrologic genome map”, EnvironmentalModelling & Software, Vol. 69, pp. 342–352, July 2015.

71. Amir-Hasan Kakaee, Pourya Rahnama, Amin Paykani and Behrooz Mashadi, “Combining artificial neural network andmulti-objective optimization to reduce a heavy-duty diesel engine emissions and fuel consumption”, Journal of CentralSouth University, Vol. 22, No. 11, pp. 4235–4245, November 2015.

72. Asad Mohammadi, Mohammad Nabi Omidvar, Xiaodong Li and Kalyanmoy Deb, “Sensitivity Analysis of Penalty-BasedBoundary Intersection on Aggregation-Based EMO Algorithms”, in 2015 IEEE Congress on Evolutionary Computation(CEC’2015), pp. 2891–2898, IEEE Press, Sendai, Japan, 25-28 May 2015, ISBN 978-1-4799-7492-4.

73. Shana Schlottfeldt, Maria Emilia M.T. Walter, Andre Carlos P.L.F. de Carvalho, Thannya N. Soares, Mariana P.C.Telles, Rafael D. Loyola and Jose Alexandre F. Diniz, “Multi-objective optimization for plant germplasm collectionconservation of genetic resources based on molecular variability”, Tree Genetics & Genomes, Vol. 11, No. 2, ArticleNumber: 16, April 2015.

74. Guilherme P. Coelho, Ana Estela A. da Silva and Fernando J. Von Zuben, “Evolving Phylogenetic Trees: A Multiob-jective Approach”, in Marie-France Sagot and Maria Emilia M.T. Walter (Editors), Second Brazilian Symposium onBioinformatics, BSB 2007, pp. 113–125, Springer. Lecture Notes in Computer Science Vol. 4643, Angra dos Reis,Brazil, August 29-31, 2007, ISBN 978-3-540-73730-8.

75. Yang Yu, Hui Ma and Mengjie Zhang, “F-MOGP: A Novel Many-Objective Evolutionary Approach to QoS-aware DataIntensive Web Service Composition”, in 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pp. 2843–2850,IEEE Press, Sendai, Japan, 25-28 May 2015, ISBN 978-1-4799-7492-4.

76. Joshua T. Knight, David J. Singer and Matthew D. Collette, “Testing of a spreading mechanism to promote diversity inmulti-objective particle swarm optimization”, Optimization and Engineering, Vol. 16, No. 2, pp. 279–302, June 2015.

77. Abolfazl Khalkhali, Majid Mostafapour, Seyed Mohamad Tabatabaie and Behnam Ansari, “Multi-objective crashwor-thiness optimization of perforated square tubes using modified NSGAII and MOPSO”, Structural and MultidisciplinaryOptimization, Vol. 54, No. 1, pp. 45–61, July 2016.

78. Hiroyuki Sato, “Analysis of inverted PBI and comparison with other scalarizing functions in decomposition basedMOEAs”, Journal of Heuristics, Vol. 21, No. 6, pp. 819–849, December 2015.

79. Erdong Yu, Qing Fei, Hongbin Ma and Qingbo Geng, “Improving Constraint Handling for Multiobjective Particle SwarmOptimization”, 2014 33rd Chinese Control Conference (CCC’2014), pp. 8622–8627, IEEE Press, Nanjing, China, July28-30, 2014.

80. Minami Miyakawa, Keiki Takadama and Hiroyuki Sato, “Controlling selection areas of useful infeasible solutions fordirected mating in evolutionary constrained multi-objective optimization”, Annals of Mathematics and Artificial Intel-ligence, Vol. 76, Nos. 1-2, pp. 25–46, February 2016.

81. Oliver Schütze, Christian Dominguez-Medina, Nareli Cruz-Cortes, Luis Gerardo de la Fraga, Jian-Qiao Sun, GregorioToscano, Ricardo Landa, “A scalar optimization approach for averaged Hausdorff approximations of the Pareto front”,Engineering Optimization, Vol. 48, No. 9, pp. 1593–1617, 2016.

82. David Hadka and Patrick Reed, “Large-scale parallelization of the Borg multiobjective evolutionary algorithm to enhancethe management of complex environmental systems”, Environmental Modelling & Software, Vol. 69, pp. 353–369, July2015.

83. Feifei Zheng, Aaron C. Zecchin, Holger R. Maier and Angus R. Simpson, “Comparison of the Searching Behavior ofNSGA-II, SAMODE, and Borg MOEAs Applied to Water Distribution System Design Problems”, Journal of WaterResources Planning and Management, Vol. 142, No. 7, Article Number: 04016017, July 2016.

84. Iraklis-Dimitrios Psychas, Eleni Delimpasi and Yannis Marinakis, “Hybrid evolutionary algorithms for the MultiobjectiveTraveling Salesman Problem”, Expert Systems with Applications, Vol. 42, No. 22, pp. 8956–8970, December 1, 2015.

85. Roberto Santana, Alexander Mendiburu and Jose A. Lozano, “Evolving MNK-landscapes with Structural Constraints”,in 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pp. 1364–1371, IEEE Press, Sendai, Japan, 25-28May 2015, ISBN 978-1-4799-7492-4.

86. Cai Dai and Yiping Wang, “A new uniform evolutionary algorithm based on decomposition and CDAS for many-objectiveoptimization”, Knowledge-Based Systems, Vol. 85, pp. 131–142, September 2015.

87. David Hadka, Jonathan Herman, Patrick Reed and Klaus Keller, “An open source framework for many-objective robustdecision making”, Environmental Modelling & Software, Vol. 74, pp. 114–129, December 2015.

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88. Evgenii S. Matrosov, Ivana Huskova, Joseph R. Kasprzyk, Julien J. Harou, Chris Lambert and Patrick M. Reed, “Many-objective optimization and visual analytics reveal key trade-offs for London’s water supply”, Journal of Hydrology, Vol.531, pp. 1040–1053, December 2015.

89. Yicha Zhang, Weijun Wang and Alain Bernard, “Embedding Multi-Attribute Decision Making into Evolutionary Opti-mization to Solve the Many-Objective Combinatorial Optimization Problems”, Journal of Grey System, Vol. 28, No. 3,pp. 124–143, 2016.

90. Rebecca Smith, Joseph Kasprzyk and Edith Zagona, “Many-Objective Analysis to Optimize Pumping and Releases inMultireservoir Water Supply Network”, Journal of Water Resources Planning and Management, Vol. 142, No. 2, ArticleNumber: 04015049, February 2016.

91. Haitham Seada and Kalyanmoy Deb, “A Unified Evolutionary Optimization Procedure for Single, Multiple, and ManyObjectives”, IEEE Transactions on Evolutionary Computation, Vol. 20, No. 3, pp. 358–369, June 2016.

92. Gian Fritsche, Andrei Strickler, Aurora Pozo and Roberto Santana, “Capturing Relationships in Multi-objective Opti-mization”, in 2015 Brazilian Conference on Intelligent Systems (BRACIS), pp. 222–227, IEEE Press, Natal, Brazil, 4-7November 2015, ISBN 978-1-5090-0016-6.

93. E.B. Schlunz, P.M. Bokov and J.H. van Vuuren, “A comparative study on multiobjective metaheuristics for solvingconstrained in-core fuel management optimisation problems”, Computers & Operations Research, Vol. 75, pp. 174–190,November 2016.

94. Seyyedeh Newsha Ghoreishi, Jan Corfixen Sørensen and Bo Nørregaard Jørgensen, “Comparative Study of Evolution-ary Multi-Objective Optimization Algorithms for a Non-Linear Greenhouse Climate Control Problem”, in 2015 IEEECongress on Evolutionary Computation (CEC’2015), pp. 1909–1917, IEEE Press, Sendai, Japan, 25-28 May 2015, ISBN978-1-4799-7492-4.

95. Maria-Guadalupe Mart́ınez-Peñaloza and Efrén Mezura-Montes, “Immune Generalized Differential Evolution for Dy-namic Multiobjective Optimization Problems”, in 2015 IEEE Congress on Evolutionary Computation (CEC’2015), pp.1918–1925, IEEE Press, Sendai, Japan, 25-28 May 2015, ISBN 978-1-4799-7492-4.

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