Exploration and exploitation in evolutionary algorithms: A survey

ACM Computing Surveys

Volumn 45, Issue 3, 2013, Pages

  Crepinsek, Matej ^a   Liu, Shih Hsi ^b   Mernik, Marjan ^a  

^a UNIVERSITY OF MARIBOR   (Slovenia)

^b CALIFORNIA STATE UNIVERSITY   (United States)

Author keywords

Diversity; Evolutionary algorithms; Exploration and exploitation

Indexed keywords

DIVERSITY; EVOLUTIONARY ALGORITHMS (EAS); EXPLORATION AND EXPLOITATION;

COMPUTER SCIENCE; EVOLUTIONARY ALGORITHMS;

SURVEYS;

EID: 84880120844     PISSN: 03600300     EISSN: 15577341     Source Type: Journal    
DOI: 10.1145/2480741.2480752     Document Type: Review

References (173)

1. Adra, S. F. And Fleming, P. J. 2011. Diversity Management In Evolutionary Many-Objective Optimization. Ieee Trans. Evol. Comput. 15, 2, 183-195.
2. Alba, E. And Dorronsoro, B. 2005. The Exploration/Exploitation Tradeoff In Dynamic Cellular Genetic Algorithms. Ieee Trans. Evol. Comput. 9, 3, 126-142.
3. Amor, H. B. And Rettinger, A. 2005. Intelligent Exploration For Genetic Algorithms: Using Self-Organizing Maps In Evolutionary Computation. In Proceedings Of The 7Th Genetic And Evolutionary Computation Conference. 1531-1538.
4. Araujo, L. And Merelo, J. J. 2011. Diversity Through Multiculturality: Assessing Migrant Choice Policies In An Island Model. Ieee Trans. Evol. Comput. 15, 4, 456-468.
5. Back, T. 1994. Selective Pressure In Evolutionary Algorithms: A Characterization Of Selection Mechanisms. In Proceedings Of The 1St Conference On Evolutionary Computing. 57-62.
6. Back, T. 1996. Evolutionary Algorithms In Theory And Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press.
7. Back, T., Eiben, A. E., And Van Der Vaart, N. A. L. 2000. An Empirical Study On Gas Without Parameters. In Proceedings Of The 6Th International Conference On Parallel Problem Solving From Nature, Springer- Verlag, 315-324.
8. Back, T. And Schwefel, H.-P. 1993. An Overview Of Evolutionary Algorithms For Parameter Optimization. Evol. Comput. 1, 1, 1-23.
9. Bartz-Beielstein, T., Lasarczyk, C. W. G., And Preuss, M. 2005. Sequential Parameter Optimization. In Proceedings Of The Ieee Congress On Evolutionary Computation. 773-780.
10. Becerra, R. L. And Coello Coello, C. A. 2006. Cultured Differential Evolution For Constrained Optimization. Comput. Methods Appl. Mech. Eng. 195, 33-36, 4303-4322.
11. Bersano-Begey, T. 1997. Controlling Exploration, Diversity And Escaping Local Optima In Gp: Adopting Weights Of Training Sets To Model Resource Consumption. In Proceedings Of The Late Breaking Papers At The Genetic Programming Conference. 7-10.
12. Beyer, H.-G. And Deb, K. 2001. On Self-Adaptive Features In Real-Parameter Evolutionary Algorithms. Ieee Trans. Evol. Comput. 5, 3, 250-270.
13. Birattari, M., Stutzle, T., Paquete, L., And Varrentrapp, K. 2002. A Racing Algorithm For Configuring Metaheuristics. In Proceedings Of The Genetic And Evolutionary Computation Conference. 11-18.
14. Blum, C., Puchinger, J., Raidl, G. A., And Roli, A. 2011. Hybrid Metaheuristics In Combinatorial Optimization: A Survey. Appl. Soft Comput. 11, 6, 4135-4151.
15. Blum, C. And Roli, A. 2003. Metaheuristics In Combinatorial Optimization: Overview And Conceptual Comparison. Acm Comput. Surv. 35, 3, 268-308.
16. Bogon, T.,Poursanidis, G., Lattner, A. D., And Timm, I. J. 2011. Extraction Of Function Features For An Automatic Configuration Of Particle Swarm Optimization. In Proceedings Of The 3Rd International Conference On Agents And Artificial Intelligence. 51-60.
17. Bosman, P. And Thierens, D. 2003. The Balance Between Proximity And Diversity In Multiobjective Evolutionary Algorithms. Ieee Trans. Evol. Comput. 7, 2, 174-188.
18. Brest, J., Greiner, S., Boskovic, B., Mernik, M., And Zumer, V. 2006. Self-Adapting Control Parameters In Differential Evolution: A Comparative Study On Numerical Benchmark Problems. Ieee Trans. Evol. Comput. 10, 6, 646-657.
19. Burke, E., Gustafson, S., And Kendall, G. 2004. Diversity In Genetic Programming: An Analysis Of Measures And Correlation With Fitness. Ieee Trans. Evol. Comput. 8, 1, 47-62.
20. Burke, E., Gustafson, S., Kendall, G., And Krasnogor, N. 2002. Advanced Population Diversity Measures In Genetic Programming. In Proceedings Of Parallel Problem Solving From Nature, Lecture Notes In Computer Science, Vol. 2439, Springer, 341-350.
21. Calegary, P., Coray, G., Hertz, A., Kobler, D., And Kuonen, P. 1999. A Taxonomy Of Evolutionary Algorithms In Combinatorial Optimization. J. Heuristics 5, 2, 145-158.
22. Chaiyaratana, N., Piroonratana, T., And Sangkawelert, N. 2007. Effects Of Diversity Control In Single-Objective And Multi-Objective Genetic Algorithms. J. Heuristics 13, 1-34.
23. Chen, G., Low, C. P., And Yang, Z. 2009. Preserving And Exploiting Genetic Diversity In Evolutionary Programming Algorithms. Ieee Trans. Evol. Comput. 13, 3, 661-673.
24. Chow, C. K. And Yuen, S. Y. 2011. An Evolutionary Algorithm That Makes Decision Based On The Entire Previous Search History. Ieee Trans. Evol. Comput. 15, 6, 741-769.
25. Cobb, H. G. And Grefenstette, J. J. 1993. Genetic Algorithms For Tracking Changing Environments. In Proceedings Of The 5Th International Conference On Genetic Algorithms. 523-530.
26. Crepin Sek, M., Mernik, M., And Liu, S.-H. 2011. Analysis Of Exploration And Exploitation In Evolutionary Algorithms By Ancestry Trees. Int. J. Innovative Comput. Appl. 3, 1, 11-19.
27. Curran, D. And O'riordan, C. 2006. Increasing Population Diversity Through Cultural Learning. Adapt. Behav. 14, 4, 315-338.
28. Czarn, A.,Macnish, C.,Vijayan, K.,Turlach, B., And Gupta, R. 2004. Statistical Exploratory Analysis Of Genetic Algorithms. Ieee Trans. Evol. Comput. 8, 4, 405-421.
29. Darwen, P. J. And Yao, X. 2001. Why More Choices Cause Less Cooperation In Iterated Prisoner's Dilemma. In Proceedings Of The Congress Of Evolutionary Computation. 987-994.
30. De Jong, E. D.,Watson, R. A., And Pollack, J. B. 2001. Reducing Bloat And Promoting Diversity Using Multiobjective Methods. In Proceedings Of The 3Rd Genetic And Evolutionary Computation Conference. 11-18.
31. De Jong, K. A. 1975. An Analysis Of The Behavior Of A Class Of Genetic Adaptive Systems. Ph.D. Dissertation, University Of Michigan, Ann Arbor, Mi.
32. De Jong, K. A. 2002. Evolutionary Computation. Mit Press, Cambridge, Ma.
33. De Jong, K. A. And Spears,W. 1992. A Formal Analysis Of The Role Of Multi-Point Crossover In Genetic Algorithms. Ann. Math. Artif. Intell. 5, 1, 1-26.
34. Deb, K. And Goldberg, D. E. 1989. An Investigation Of Niche And Species Formation In Genetic Function Optimization. In Proceedings Of The 3Rd International Conference On Genetic Algorithms. 42-50.
35. D'haeseleer, P. And Bluming, J. 1994. Advances In Genetic Programming. Mit Press, Cambridge, Ma.
36. Dŕeo, J. 2009. Using Performance Fronts For Parameter Setting Of Stochastic Metaheuristics. In Proceedings Of The 11Th Annual Conference Companion On Genetic And Evolutionary Computation Conference. 2197- 2200.
37. Eiben, A. E., Hinterding, R., And Michalewicz, Z. 1999. Parameter Control In Evolutionary Algorithms. Ieee Trans. Evol. Comput. 3, 2, 124-141.
38. Eiben, A. E., Marchiori, E., And Valko, V. A. 2004. Evolutionary Algorithms With On-The-Fly Population Size Adjustment. In Proceedings Of Parallel Problem Solving From Nature, Lecture Notes In Computer Science, Vol. 3242, Springer, 41-50.
39. Eiben, A. E. And Schippers, C. 1998. On Evolutionary Exploration And Exploitation. Fundamenta Informaticae 35, 35-50.
40. Eiben, A. E. And Smit, S. K. 2011. Parameter Tuning For Configuring And Analyzing Evolutionary Algorithms. Swarm Evol. Comput. 1, 1, 19-31.
41. Eiben, A. E. And Smith, J. E. 2008. Introduction To Evolutionary Computing. Springer, Berlin.
42. Eshelman, L. J. 1991. The Chc Adaptive Search Algorithm: How To Have Safe Search When Engaging In Nontraditional Genetic Recombination. Found. Genetic Algorith. 1, 265-283.
43. Eshelman, L. J. And Schaffer, J. 1991. Preventing Premature Convergence In Genetic Algorithms By Preventing Incest. In Proceedings Of The 4Th International Conference On Genetic Algorithms. 115-122.
44. Fernandez-Prieto, J. A., Canada-Bago, J., Gadeo-Martos, M. A., And Velasco, J. R. 2011. Optimisation Of Control Parameters For Genetic Algorithms To Test Computer Networks Under Realistic Traffic Loads. Appl. Soft Comput. 11, 4, 3744-3752.
45. Fister, I., Mernik, M., And Filipic, B. 2010. A Hybrid Self-Adaptive Evolutionary Algorithm For Marker Optimization In The Clothing Industry. Appl. Soft Comput. 10, 2, 409-422.
46. Fogarty, T. C. 1989. Varying The Probability Of Mutation In The Genetic Algorithm. In Proceedings Of The 3Rd International Conference On Genetic Algorithms. 104-109.
47. Fogel, L. J. 1999. Intelligence Through Simulated Evolution: Forty Years Of Evolutionary Programming. Wiley, Itoboken, Nj.
48. Fonseca, C. M. And Fleming, P. J. 1995. Multiobjective Genetic Algorithms Made Easy: Selection, Sharing And Mating Restriction. In Proceedings Of The 1St International Conference On Genetic Algorithms In Engineering Systems: Innovations And Applications. 45-52.
49. Freisleben, B. Andmerz, P. 1996. A Genetic Local Search Algorithm For Solving Symmetric And Asymmetric Traveling Salesman Problems. In Proceedings Of The International Conference On Evolutionary Computation. 616-621.
50. Friedrich, T., Hebbinghaus, N., And Neumann, F. 2007. Rigorous Analyses Of Simple Diversity Mechanisms. In Proceedings Of The Genetic And Evolutionary Computation Conference. 1219-1225.
51. Friedrich, T., Oliveto, P. S., Sudholt, D., And Witt, C. 2008. Theoretical Analysis Of Diversity Mechanisms For Global Exploration. In Proceedings Of The Genetic And Evolutionary Computation Conference. 945-952.
52. Galv́an-Ĺopez, E.,Mcdermott, J., O'neill, M., And Brabazon, A. 2010. Towards An Understanding Of Locality In Genetic Programming. In Proceedings Of The 12Th Annual Conference On Genetic And Evolutionary Computation. 901-908.
53. Gao, H. And Xu, W. 2011. Particle Swarm Algorithm With Hybrid Mutation Strategy. Appl. Soft Comput. 11, 8, 5129-5142.
54. Gen, M. And Cheng, R. 1997. Genetic Algorithms And Engineering Design. John Wiley And Sons, Itoboken, Nj.
55. Ghosh, A., Tsutsui, S., And Tanaka, H. 1996. Individual Aging In Genetic Algorithms. In Proceedings Of The Australian New Zealand Conference On Intelligent Information Systems. 276-279.
56. Goh, K. S., Lim, A., And Rodrigues, B. 2003. Sexual Selection For Genetic Algorithms. Artif. Intell. Rev. 19, 123-152.
57. Goldberg,D. E. 2008. Genetic Algorithms In Search, Optimization And Machine Learning. Dorling Kindersley, London.
58. Goldberg, D. E. And Deb, K. 1991. A Comparative Analysis Of Selection Schemes Used In Genetic Algorithms. In Foundations Of Genetic Algorithms. Morgan Kaufmann, Burlington, Ma, 69-93.
59. Goldberg, D. E. And Richardson, J. 1987. Genetic Algorithms With Sharing For Multimodal Function Optimization. In Proceedings Of The 2Nd International Conference On Genetic Algorithms. 41-49.
60. Gong, W., Cai, Z., And Jiang, L. 2008. Enhancing The Performance Of Differential Evolution Using Orthogonal Design Method. Appl. Math. Comput. 206, 1, 56-69.
61. Greenwood, G. W., Fogel, G. B., And Ciobanu, M. 1999. Emphasizing Extinction In Evolutionary Programming. In Proceedings Of The Congress Of Evolutionary Computation. 666-671.
62. Grefenstette, J. J. 1986. Optimization Of Control Parameters For Genetic Algorithms. Ieee Trans. Syst., Man Cybernetics 16, 1, 122-128.
63. Grefenstette, J. J. 1992. Genetic Algorithms For Changing Environments. In Proceedings Of Parallel Problem Solving From Nature, Elsevier, Amsterdam, 137-144.
64. Harik, G. R. 1995. Finding Multimodal Solutions Using Restricted Tournament Selection. In Proceedings Of The 6Th International Conference On Genetic Algorithms. 24-31.
65. Harik, G. R. And Lobo, F. 1999. A Parameter-Less Genetic Algorithm. Tech. Rep., University Of Illinois At Urbana-Champaign, Il.
66. Harik, G. R., Lobo, F., And Goldberg, D. E. 1999. The Compact Genetic Algorithm. Ieee Trans. Evol. Comput. 3, 4, 287-297.
67. Hart,W. E. 1994. Adaptive Global Optimization With Local Search. Ph.D. Dissertation, University Of California, San Diego, Ca.
68. Herrera, F. And Lozano, M. 1996. Adaptation Of Genetic Algorithm Parameters Based On Fuzzy Logic Controllers. Genetic Algorith. Soft Comput. 95-125.
69. Hesser, J. And Manner, R. 1991. Toward An Optimal Mutation Probability For Genetic Algorithms. In Proceedings Of The 1St Conference Of Parallel Problem Solving From Nature, Lecture Notes In Computer Science, Vol. 496, Springer, 23-32.
70. Holland, J. H. 1975. Adaptation In Natural And Artificial Systems. University Of Michigan Press, Ann Arbor, Mi.
71. Horn, J., Nafpliotis, N., And Goldberg, D. E. 1994. A Niched Pareto Genetic Algorithm For Multiobjective Optimization. In Proceedings Of The 1St Ieee Conference On Evolutionary Computation. 82-87.
72. Hutter, M. And Legg, S. 2006. Fitness Uniform Optimization. Ieee Trans. Evol. Comput. 10, 5, 568-589.
73. Ishibuchi, H.,Hitotsuyanagi, Y., Wajamatsu, Y., And Nojima, Y. 2010A. How To Choose Solutions For Local Search In Multiobjective Combinatorial Memetic Algorithms. In Proceedings Of The 11Th Conference Of Parallel Problem Solving From Nature: Part I, Lecture Notes In Computer Science, Vol. 6238, Springer, 516-525.
74. Ishibuchi, H., Narukawa, K., Tsukamoto, N., And Nojima, Y. 2008. An Empirical Study On Similarity-Based Mating For Evolutionary Multiobjective Combinatorial Optimization. Europ. J. Oper. Res. 188, 1, 57-75.
75. Ishibuchi, H.,Tsukamoto, N., And Nojima, Y. 2010B. Diversity Improvement By Non-Geometric Binary Crossover In Evolutionary Multiobjective Optimization. Ieee Trans. Evol. Comput. 14, 6, 985-998.
76. Ishibuchi, H., Yoshida, T., And Murata, T. 2003. Balance Between Genetic Search And Local Search In Memetic Algorithms For Multiobjective Permutation Flowshop Scheduling. Ieee Trans. Evol. Comput. 7, 2, 204- 223.
77. Jia, D., Zheng, G., And Khan, M. K. 2011. An Effective Memetic Differential Evolution Algorithm Based On Chaotic Local Search. Inform. Sci. 181, 15, 3175-3187.
78. Jin, X. And Reynolds, R. 1999. Using Knowledge-Based Evolutionary Computation To Solve Nonlinear Constraint Optimization Problems: A Cultural Algorithm Approach. In Proceedings Of The Congress On Evolutionary Computation. 1672-1678.
79. Joan-Arinyo, R., Luzon, M. V., And Yeguas, E. 2011. Parameter Tuning Of Pbil And Chc Evolutionary Algorithms Applied To Solve The Root Identification Problem. Appl. Soft Comput. 11, 1, 754-767.
80. Jose-Revuelta, L. M. S. 2007. A New Adaptive Genetic Algorithm For Fixed Channel Assignment. Inform. Sci. 177, 2655-2678.
81. Kohonen, T. 2001. Self-Organizing Maps 3Rd Ed. Springer-Verlag, New York, Ny.
82. Koumousis, V. And Katsaras, C. P. 2006. A Saw-Tooth Genetic Algorithm Combining The Effects Of Variable Population Size And Reinitialization To Enhance Performance. Ieee Trans. Evol. Comput. 10, 1, 19-28.
83. Koza, J. R. 1992. Genetic Programming: On The Programming Of Computers By Means Of Natural Selection. Mit Press, Cambridge, Ma.
84. Krasnogor, N. And Smith, J. 2005. A Tutorial For Competent Memetic Algorithms: Model, Taxonomy, And Design Issues. Ieee Trans. Evol. Comput. 9, 5, 474-488.
85. Krink, T., Rickers, P., And Thomsen, R. 2000. Applying Self-Organised Criticality To Evolutionary Algorithms. In Proceedings Of The 6Th International Conference On Parallel Problem Solving From Nature, Springer- Verlag, New York, Ny, 375-384.
86. Langdon, W. B. 1998. Data Structures And Genetic Programming: Genetic Programming + Data Structures = Automatic Programming. Kluwer, Alphen Aan Den Rjin, Netherlands.
87. Lee, J.-Y., Kim, M.-S., And Lee, J.-J. 2011. Compact Genetic Algorithm Using Belief Vectors. Appl. Soft Comput. 11, 4, 3385-3401.
88. Leung, Y., Gao, Y., And Xu, Z. 1997. A Perspective On Premature Convergence In Genetic Algorithms And Its Markov Chain Analysis. Trans. Neural Netw. 8, 5, 1165-1176.
89. Leung, Y. And Wang, Y. 2001. An Orthogonal Genetic Algorithm With Quantization For Global Numerical Optimization. Ieee Trans. Evol. Comput. 5, 1, 41-53.
90. Li, J.-P., Balazs, M. E., Parks, G. T., And Clarkson, J. P. 2002. A Species Conserving Genetic Algorithm For Multimodal Function Optimization. Evol. Comput. 10 3, 207-234.
91. Li, M., Cai, Z., And Sun, G. 2004. An Adaptive Genetic Algorithm With Diversity-Guided Mutation And Its Global Convergence Property. J. Central South Univ. Technol. 11, 3, 323-327.
92. Li, Z. And Wang, X. 2011. Chaotic Differential Evolution Algorithm For Solving Constrained Optimization Problems. Inform. Technol. J. 10, 12, 2378-2384.
93. Liang, Y. And Leung, K.-S. 2011. Genetic Algorithm With Adaptive Elitist-Population Strategies For Multimodal Function Optimization. Appl. Soft Comput. 11, 2, 2017-2034.
94. Liao, T.W. 2010. Two Hybrid Differential Evolution Algorithms For Engineering Design Optimization. Appl. Soft Comput. 10, 4, 1188-1199.
95. Lin, J.-Y. And Chen, Y.-P. 2011. Analysis On The Collaboration Between Global Search And Local Search In Memetic Computation. Ieee Trans. Evol. Comput. 15, 5, 608-622.
96. Liu, S.-H., Mernik, M., And Bryant, B. R. 2004. Parameter Control In Evolutionary Algorithms By Domainspecific Scripting Language Ppcea. In Proceedings Of The International Conference On Bioinspired Optimization Methods And Their Applications. 41-50.
97. Liu, S.-H., Mernik, M., And Bryant, B. R. 2007. A Clustering Entropy-Driven Approach For Exploring And Exploiting Noisy Functions. In Proceedings Of The 22Nd Acm Symposium On Applied Computing. 738-742.
98. Liu, S.-H., Mernik, M., And Bryant, B. R. 2009. To Explore Or To Exploit: An Entropy-Driven Approach For Evolutionary Algorithms. Int. J. Knowl. Intell. Eng. Syst. 13, 3, 185-206.
99. Lobo, F. J., Lima, C. F., And Michalewicz, Z. 2007. Parameter Setting In Evolutionary Algorithms. Springer, Berlin.
100. Lozano, M., Herrera, F., And Cano, J. R. 2008. Replacement Strategies To Preserve Useful Diversity In Steadystate Genetic Algorithms. Inform. Sci. 178, 23, 4421-4433.
101. Luerssen, M. H. 2005. Phenotype Diversity Objectives For Graph Grammar Evolution. In Recent Advances In Artificial Life, World Scientific Publishing, Singapore, 159-170.
102. Mahfoud, S. W. 1995. Niching Methods For Genetic Algorithms. Tech. Rep., University Of Illinois At Urbana Champaign, Il.
103. Majig, M. And Fukushima, M. 2008. Adaptive Fitness Function For Evolutionary Algorithm And Its Applications. In Proceedings Of The International Conference On Informatics Education And Research For Knowledge- Circulating Society. 119-124.
104. Mallipeddi, R., Suganthan, P. N., Pan, Q. K., And Tasgetiren, M. F. 2011. Differential Evolution Algorithm With Ensemble Of Parameters And Mutation Strategies. Appl. Soft Comput. 11, 2, 1679-1696.
105. Martin, W. N., Lienig, J., And Cohoon, J. P. 1999. Island (Migration) Models: Evolutionary Algorithms Based On Punctuated Equilibria (In Handbook Of Evolutionary Computation). Oxford University Press.
106. Mashinchi, M. H., Orgun, M. A., And Pedrycz,W. 2011. Hybrid Optimization With Improved Tabu Search. Appl. Soft Comput. 11, 2, 1993-2006.
107. Masisi, L., Nelwamondo, V., And Marwala, T. 2008. The Use Of Entropy To Measure Structural Diversity. In Proceedings Of The Ieee International Conference On Computational Cybernetics. 41-45.
108. Matsui, K. 1999. New Selection Method To Improve The Population Diversity In Genetic Algorithms. In Proceedings Of Ieee International Conference On Systems, Man And Cybernetics. 625-630.
109. Mattiussi, C.,Waibel,M., And Floreano, D. 2004. Measures Of Diversity For Populations And Distances Between Individuals With Highly Reorganizable Genomes. Evol. Comput. 12, 4, 495-515.
110. Mauldin, M. L. 1984. Maintaining Diversity In Genetic Search. In Proceedings Of The National Conference On Artificial Intelligence. 247-250.
111. Mcginley, B.,Maher, J., O'riordan, C., And Morgan, F. 2011. Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, And Selection. Ieee Trans. Evol. Computat. 15, 5, 692-714.
112. Mcphee, N. F. And Hopper, N. J. 1999. Analysis Of Genetic Diversity Through Population History. In Proceedings Of The 1St Genetic And Evolutionary Computation Conference. 1112-1120.
113. Mengshoel, O. J. Andgoldberg, D. E. 1999. Probabilistic Crowding: Deterministic Crowding With Probabilisitic Replacement. In Proceedings Of The Genetic And Evolutionary Computation Conference. 409-416.
114. Mernik, M., Heering, J., And Sloane, A. 2005. When And How To Develop Domain-Specific Languages. Acm Comput. Sur. 37, 4, 316-344.
115. Merz, P. And Freisleben, B. 2000. Fitness Landscape Analysis And Memetic Algorithms For The Quadratic Assignment Problem. Ieee Trans. Evol. Comput. 4, 4, 337-352.
116. Michalewicz, Z. 1996. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, New York, Ny.
117. Misevicius, A. 2011. Generation Of Grey Patterns Using An Improved Genetic-Evolutionary Algorithm: Some New Results. Inform. Technol. Control 40, 4, 330-343.
118. Misevicius, A. And Rubliauskas, D. 2008. Enhanced Improvement Of Individuals In Genetic Algorithms. Inform. Technol. Control 37, 3, 179-186.
119. Mongus, D., Repnik, B.,Mernik, M., And Zalik, B. 2012. A Hybrid Evolutionary Algorithm For Tuning A Clothsimulation Model. Appl. Soft Comput. 12, 1, 266-273.
120. Montero, E. And Riff, M.-C. 2011. On-The-Fly Strategies For Evolutionary Algorithms. Inform. Sci. 181, 552- 566.
121. Moraglio, A., Kim, Y.-H., Yoon, Y., And Moon, B.-R. 2007. Geometric Crossovers For Multiway Graph Partitioning. Evol. Comput. 15, 4, 445-474.
122. Mori, N., Yoshida, J., Tamaki, H., Kita, H., And Nishikawa, Y. 1995. A Thermodynamical Selection Rule For The Genetic Algorithm. In Proceedings Of The 2Nd Ieee International Conference On Evolutionary Computation. 188-192.
123. Moscato, P. 1999. Memetic Algorithms: A Short Introduction. In New Ideas In Optimization, Mcgraw Hill Ltd., Maidenhead, U.K., 219-234.
124. Muhlenbein, H. And Paass, G. 1996. From Recombination Of Genes To The Estimation Of Distributions I. Binary Parameters. In Proceedings Of Parallel Problem Solving From Nature, Lecture Notes In Computer Science, Vol. 1141, Springer, 178-187.
125. Nannen, V. And Eiben, A. E. 2006. A Method For Parameter Calibration And Relevance Estimation In Evolutionary Algorithms. In Proceedings Of The 8Th Annual Conference On Genetic And Evolutionary Computation. 183-190.
126. Nguyen, Q. H., Ong, Y.-S., And Lim, M. H. 2009. A Probabilistic Memetic Framework. Ieee Trans. Evol. Comput. 13, 3, 604-623.
127. Ochoa, G., Bilgin, B., And Korkmaz, E. E. 2008. A Comprehensive Analysis Of Hyper-Heuristics. Intell. Data Anal. 12, 1, 3-23.
128. Ong, Y.-S., Lim, M.-H., Zhu, N., And Wong, K.-W. 2006. Classification Of Adaptive Memetic Algorithms: A Comparative Study. Ieee Trans. Syst. Man Cybernetics (Part B), 141-152.
129. Oppacher, F. And Wineberg, M. 1999. The Shifting Balance Ga: Improving The Ga In Dynamic Environment. In Proceedings Of The 1St Genetic And Evolutionary Computation Conference. 504-510.
130. Paenke, I., Jin, Y., And Branke, J. 2009. Balancing Population- And Individual-Level Adaptation In Changing Environments. Adapt. Behav. 17, 2, 153-174.
131. Pan, Q.-K., Suganthan, P. N.,Wang, L., Gao, L., And Mallipeddi, R. 2011. A Differential Evolution Algorithm With Self-Adapting Strategy And Control Parameters. Comput. Oper. Res. 38, 1, 394-408.
132. Petrowski, A. 1996. A Clearing Procedure As A Niching Method For Genetic Algorithms. In Proceedings Of The Ieee International Conference On Evolutionary Computation. 798-803.
133. Qin, A. K., Huang, V. L., And Suganthan, P. N. 2009. Differential Evolution Algorithm With Strategy Adaptation For Global Numerical Optimization. Ieee Trans. Evol. Comput. 13, 2, 398-417.
134. Rahnamayan, S., Tizhoosh, H. R., And Salama, M. M. A. 2008. Opposition-Based Differential Evolution. Ieee Trans. Evol. Comput. 12, 1, 64-79.
135. Ramsey, C. L. And Grefenstette, J. J. 1993. Case-Based Initialization Of Genetic Algorithms. In Proceedings Of The 5Th International Conference On Genetic Algorithms. 84-91.
136. Ronald, E. 1995. When Selection Meets Seduction. In Proceedings Of The 6Th International Conference On Genetic Algorithms. 167-173.
137. Rosca, J. 1995. Entropy-Driven Adaptive Representation. In Proceedings Of Theworkshop On Genetic Programming: From Theory To Real-World Applications, 23-32.
138. Sareni, B. And Krahenbuhl, L. 1998. Fitness Sharing And Niching Methods Revisited. Ieee Trans. Evol. Comput. 2, 3, 97-106.
139. Schaffer, J. D., Caruana, R. A., Eshelman, L. J., And Das, R. 1989. A Study Of Control Parameters Affecting Online Performance Of Genetic Algorithms For Function Optimization. In Proceedings Of The 3Rd International Conference On Genetic Algorithms. 51-60.
140. Shimodaira, H. 1997. Dcga: A Diversity Control Oriented Genetic Algorithm. In Proceedings Of The 2Nd International Conference On Genetic Algorithms In Engineering Systems: Innovations And Applications. 444-449.
141. Singh, G. And Deb, K. 2006. Comparison Of Multi-Modal Optimization Algorithms Based On Evolutionary Algorithms. In Proceedings Of The 8Th Annual Conference On Genetic And Evolutionary Computation. 1305-1312.
142. Smit, S. K. And Eiben, A. E. 2009. Comparing Parameter Tuning Methods For Evolutionary Algorithms. In Proceedings Of The Ieee Congress On Evolutionary Computation. 399-406.
143. Smith, J. E. And Fogarty, T. C. 1997. Operator And Parameter Adaptation In Genetic Algorithms. Soft Comput. 1, 2, 81-87.
144. Smith, R. E., Forrest, S., And Perelson, A. S. 1993. Searching For Diverse, Cooperative Subpopulations With Genetic Algorithms. Evol. Comput. 1, 2, 127-149.
145. Smith, R. E. And Smuda, E. 1995. Adaptively Resizing Populations: Algorithm, Analysis, And First Results. Complex Syst. 9, 47-72.
146. Soza, C., Becerra, R. L., Riff, M. C., And Coello Coello, C. A. 2011. Solving Timetabling Problems Using A Cultural Algorithm. Appl. Soft Comput. 11, 1, 337-344.
147. Spears, W. M. 1995. Adapting Crossover In Evolutionary Algorithms. In Proceedings Of The Evolutionary Programming Conference. 367-384.
148. Srinivas, M. And Patnaik, L. M. 1994. Adaptive Probabilities Of Crossover And Mutation In Genetic Algorithms. Ieee Trans. Syst. Man Cybernetics 24, 656-667.
149. Storch, T. 2004. On The Choice Of The Population Size. In Proceedings Of The Genetic And Evolutionary Computation Conference. 748-760.
150. Storn, R. And Price, K. 1997. Differential Evolution - A Simple And Efficient Heuristic For Global Optimization Over Continuous Spaces. J. Global Optim. 11, 341-359.
151. Talbi, E. G. 2002. A Taxonomy Of Hybrid Metaheuristics. J. Heuristics 8, 5, 541-564.
152. Toffolo, A. And Benini, E. 2003. Genetic Diversity As An Objective In Multi-Objective Evolutionary Algorithms. Evol. Comput. 11, 2, 151-167.
153. Tsujimura, Y. And Gen, M. 1998. Entropy-Based Genetic Algorithm For Solving Tsp. In Proceedings Of The 2Nd International Conference On Knowledge-Based Intelligent Electronic Systems. 285-290.
154. Tsutsui, S., Fujimoto, Y., And Ghosh, A. 1997A. Forking Genetic Algorithms: Gas With Search Space Division Schemes. Evol. Comput. 5, 1, 61-80.
155. Tsutsui, S., Ghosh, A., Corne, D., And Fujimoto, Y. 1997B. A Real Coded Genetic Algorithm With An Explorer And An Exploiter Populations. In Proceedings Of The 7Th International Conference On Genetic Algorithms. 238-245.
156. Ursem, R. 2000. Multinational Gas: Multimodal Optimization Techniques In Dynamic Environments. In Proceedings Of The 2Nd Genetic And Evolutionary Computation Conference. 19-26.
157. Ursem, R. 2002. Diversity-Guided Evolutionary Algorithms. In Proceedings Of Parallel Problem Solving From Nature, Lecture Notes In Computer Science, Vol. 2439, Springer, 462-471.
158. Wang, Y.,Cai, Z., And Zhang,Q. 2012. Enhancing The Search Ability Of Differential Evolution Through Orthogonal Crossover. Inform. Sci. 185, 1, 153-177.
159. Watson, J., Baker, T., Bell, S., Gann, A., Levine, M., And Losick, R. 2004. Molecular Biology Of The Gene. Benjamin Cummings, San Francisco, Ca. Whitley, D.,Mathias, K., And Fitzhorn, P. 1991. Delta Coding: An Iterative Search Strategy For Genetic Algorithms. In Proceedings Of The 4Th International Conference On Genetic Algorithms. 77-84.
160. Whitley, D. And Starkweather, D. 1990. Genitor-Ii: A Distributed Genetic Algorithm. J. Exp. Theor. Artif. Intell. 2, 3, 189-214.
161. Wineberg, M. And Oppacher, F. 2003. Distance Between Populations. In Proceedings Of The International Conference On Genetic And Evolutionary Computation. 1481-1492.
162. Wong, Y.-Y., Lee, K.-H., Leung, K.-S., And Ho, C.-W. 2003. A Novel Approach In Parameter Adaptation And Diversity Maintenance For Genetic Algorithms. Soft Comput. 7, 506-515.
163. Yang, S. 2008. Genetic Algorithms With Memory- And Elitism-Based Immigrants In Dynamic Environments. Evol. Comput. 16, 3, 385-416.
164. Yao, X., Liu, Y., And Lin, G. 1999. Evolutionary Programming Made Faster. Ieee Trans. Evol. Comput. 3, 2, 82-102.
165. Yin, X. And Germay, N. 1993. A Fast Genetic Algorithm With Sharing Scheme Using Cluster Analysis Method In Multi-Modal Function Optimization. In Proceedings Of The International Conference On Artificial Neural Nets And Genetic Algorithms. 450-457.
166. Yu, E. L. And Suganthan, P. N. 2010. Ensemble Of Niching Algorithms. Inform. Sci. 180, 15, 2815-2833.
167. Yuen, S. Y. And Chow, C. K. 2009. A Genetic Algorithm That Adaptively Mutates And Never Revisits. Ieee Trans. Evol. Comput. 13, 2, 454-472.
168. Zhang, J. And Sanderson, A. C. 2009. Jade: Adaptive Differential Evolution With Optional External Archive. Ieee Trans. Evol. Comput. 13, 5, 945-957.
169. Zhao, X. 2011. Simulated Annealing Algorithm With Adaptive Neighborhood. Appl. Soft Comput. 11, 2, 1827- 1836.
170. Zielinski, K., Weitkemper, P., Laur, R., And Kammeyer, K.-D. 2009. Optimization Of Power Allocation For Interference Cancellation With Particle Swarm Optimization. Ieee Trans. Evol. Comput. 8, 2, 128-150.
171. Zitzler, E., Deb, K., And Thiele, L. 2000. Comparison Of Multiobjective Evolutionary Algorithms: Empirical Results. Evol. Comput. 8, 2, 173-195.
172. Zitzler, E., Laumanns, M., And Thiele, L. 2002. Spea2: Improving The Strength Pareto Evolutionary Algorithm For Multiobjective Optimization. Evol. Meth. Design: Optim. Control, 95-100.
173. Zitzler, E. And Thiele, L. 1999. Multiobjective Evolutionary Algorithms: A Comparative Case Study And The Strength Pareto Approach. Ieee Trans. Evol. Comput. 3, 4, 257-271.