Skip to main content
main-content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2018 | OriginalPaper | Buchkapitel

15. Genetic Algorithms

verfasst von: Carlos García-Martínez, Francisco J. Rodriguez, Manuel Lozano

Erschienen in: Handbook of Heuristics

Verlag: Springer International Publishing

share
TEILEN

Abstract

This chapter presents the fundamental concepts of genetic algorithms (GAs) that have become an essential tool for solving optimization problems in a wide variety of fields. The first part of this chapter is devoted to the revision of the basic components for the design of GAs. We illustrate this construction process through its application for solving three widely known optimization problems as knapsack problem, traveling salesman problem, and real-parameter optimization. The second part of the chapter focuses on the study of diversification techniques that represent a fundamental issue in order to achieve an effective search in GAs. In fact, analyzing its diversity has led to the presentation of numerous GA models in the literature. Similarly, the hybridization with other metaheuristics and optimization methods has become a very fruitful research area. The third part of the chapter is dedicated to the study of these hybrid methods. In closing, in the fourth part, we outline the wide spectrum of application areas that shows the level of maturity and the wide research community of the GA field.
Literatur
1.
Zurück zum Zitat Adler D (1993) Genetic algorithm and simulated annealing: a marriage proposal. In: Proceedings of the IEEE international conference on neural network, pp 1104–1109 Adler D (1993) Genetic algorithm and simulated annealing: a marriage proposal. In: Proceedings of the IEEE international conference on neural network, pp 1104–1109
2.
Zurück zum Zitat Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9(2):126–142 Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9(2):126–142
3.
Zurück zum Zitat Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5):443–462 Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5):443–462
4.
Zurück zum Zitat Alba E, Troya JM (2001) Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Gener Comput Syst 17(4):451–465 Alba E, Troya JM (2001) Analyzing synchronous and asynchronous parallel distributed genetic algorithms. Future Gener Comput Syst 17(4):451–465
5.
Zurück zum Zitat Al-Naqi A, Erdogan A, Arslan T (2013) Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes. Soft Comput 17(7): 1145–1157 Al-Naqi A, Erdogan A, Arslan T (2013) Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes. Soft Comput 17(7): 1145–1157
6.
Zurück zum Zitat Araujo L, Merelo J (2011) Diversity through multiculturality: assessing migrant choice policies in an island model. IEEE Trans Evol Comput 15(4):456–469 Araujo L, Merelo J (2011) Diversity through multiculturality: assessing migrant choice policies in an island model. IEEE Trans Evol Comput 15(4):456–469
7.
Zurück zum Zitat Bäck T, Schütz M (1996) Intelligent mutation rate control in canonical genetic algorithms. In: Raś Z, Michalewicz M (eds) Foundations of intelligent systems. Lecture notes in computer science, vol 1079, pp 158–167 Bäck T, Schütz M (1996) Intelligent mutation rate control in canonical genetic algorithms. In: Raś Z, Michalewicz M (eds) Foundations of intelligent systems. Lecture notes in computer science, vol 1079, pp 158–167
8.
Zurück zum Zitat Baker J (1987) Adaptive selection methods for genetic algorithms. In: Grefenstette J (ed) International conference on genetic algorithms applications and their application. Erlbaum Associates, pp 14–21 Baker J (1987) Adaptive selection methods for genetic algorithms. In: Grefenstette J (ed) International conference on genetic algorithms applications and their application. Erlbaum Associates, pp 14–21
9.
Zurück zum Zitat Baker J (1987) Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the international conference on genetic algorithms, pp 14–21 Baker J (1987) Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the international conference on genetic algorithms, pp 14–21
10.
Zurück zum Zitat Baluja S, Caruanna R (1995) Removing the genetics from the standard genetic algorithm. In: Proceedings of the annual conference on machine learning, pp 38–46 Baluja S, Caruanna R (1995) Removing the genetics from the standard genetic algorithm. In: Proceedings of the annual conference on machine learning, pp 38–46
11.
Zurück zum Zitat Banzhaf W (1990) The “Molecular” traveling salesman. Biol Cybern 64:7–14 Banzhaf W (1990) The “Molecular” traveling salesman. Biol Cybern 64:7–14
12.
Zurück zum Zitat Beyer H, Deb K (2001) On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput 5(3):250–270 Beyer H, Deb K (2001) On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput 5(3):250–270
13.
Zurück zum Zitat Bhandarkar S, Zhang H (1999) Image segmentation using evolutionary computation. IEEE Trans Evol Comput 3(1):1–21 Bhandarkar S, Zhang H (1999) Image segmentation using evolutionary computation. IEEE Trans Evol Comput 3(1):1–21
14.
Zurück zum Zitat Blum C (2010) Hybrid metaheuristics – guest editorial. Comput Oper Res 37(3):430–431 Blum C (2010) Hybrid metaheuristics – guest editorial. Comput Oper Res 37(3):430–431
15.
Zurück zum Zitat Blum C, Puchinger J, Raidl G, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11:4135–4151 Blum C, Puchinger J, Raidl G, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11:4135–4151
16.
Zurück zum Zitat Caruana R, Schaffer J (1988) Representation and hidden bias: gray versus binary coding for genetic algorithms. In: Proceedings of the fifth international conference on machine learning, pp 153–162 Caruana R, Schaffer J (1988) Representation and hidden bias: gray versus binary coding for genetic algorithms. In: Proceedings of the fifth international conference on machine learning, pp 153–162
17.
Zurück zum Zitat Chelouah R, Siarry P (2003) Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur J Oper Res 148(2): 335–348 Chelouah R, Siarry P (2003) Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions. Eur J Oper Res 148(2): 335–348
18.
Zurück zum Zitat Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287 Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191(11–12):1245–1287
19.
Zurück zum Zitat Coello C, Lamont G, Veldhuizen D (2006) Evolutionary algorithms for solving multi-objective problems. Springer, New York Coello C, Lamont G, Veldhuizen D (2006) Evolutionary algorithms for solving multi-objective problems. Springer, New York
20.
Zurück zum Zitat Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35:1–35:33 Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35:1–35:33
21.
Zurück zum Zitat Cruz C, González JR, Pelta DA (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput 15(7):1427–1448 Cruz C, González JR, Pelta DA (2011) Optimization in dynamic environments: a survey on problems, methods and measures. Soft Comput 15(7):1427–1448
22.
Zurück zum Zitat Dantzig G (1957) Discrete variable extremum problems. Oper Res 5:266–277 Dantzig G (1957) Discrete variable extremum problems. Oper Res 5:266–277
23.
Zurück zum Zitat Davis L (1985) Adaptive algorithms to epistactic domains. In: Proceedings of the international conference on artificial intelligence, pp 162–164 Davis L (1985) Adaptive algorithms to epistactic domains. In: Proceedings of the international conference on artificial intelligence, pp 162–164
24.
Zurück zum Zitat Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester/New York Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester/New York
25.
Zurück zum Zitat Deb K (2008) Introduction to evolutionary multiobjective optimization. In: Branke J, Deb K, Miettinen K, Słowiski R (eds) Multiobjective optimization. Lecture notes in computer science, vol 5252. Springer, Berlin/Heidelberg, pp 59–96 Deb K (2008) Introduction to evolutionary multiobjective optimization. In: Branke J, Deb K, Miettinen K, Słowiski R (eds) Multiobjective optimization. Lecture notes in computer science, vol 5252. Springer, Berlin/Heidelberg, pp 59–96
26.
Zurück zum Zitat De Jong K (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan De Jong K (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan
27.
Zurück zum Zitat De Jong K (1993) Genetic algorithms are NOT function optimizers. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann, San Mateo De Jong K (1993) Genetic algorithms are NOT function optimizers. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann, San Mateo
28.
Zurück zum Zitat De Jong K, Sarma J (1993) Generation gaps revisited. In: Whitley LD (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 19–28 De Jong K, Sarma J (1993) Generation gaps revisited. In: Whitley LD (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 19–28
29.
Zurück zum Zitat Eiben A, Smith J (2003) Introduction to evolutionary computation. Natural computing series. Springer, New York Eiben A, Smith J (2003) Introduction to evolutionary computation. Natural computing series. Springer, New York
30.
Zurück zum Zitat Eiben A, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141 Eiben A, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141
31.
Zurück zum Zitat Eklund SE (2004) A massively parallel architecture for distributed genetic algorithms. Parallel Comput 30(5–6):647–676 Eklund SE (2004) A massively parallel architecture for distributed genetic algorithms. Parallel Comput 30(5–6):647–676
32.
Zurück zum Zitat Eshelman L (1991) The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. Foundations of genetic algorithms, vol 1. Morgan Kaufmann, San Mateo, CA, pp 265–283 Eshelman L (1991) The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. Foundations of genetic algorithms, vol 1. Morgan Kaufmann, San Mateo, CA, pp 265–283
33.
Zurück zum Zitat Eshelman L, Schaffer J (1991) Preventing premature convergence in genetic algorithms be preventing incest. In: Proceedings of the international conference on genetic algorithms, pp 115–122 Eshelman L, Schaffer J (1991) Preventing premature convergence in genetic algorithms be preventing incest. In: Proceedings of the international conference on genetic algorithms, pp 115–122
34.
Zurück zum Zitat Eshelman L, Schaffer J (1993) Real-coded genetic algorithms and interval-schemata. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann, San Mateo, pp 187–202 Eshelman L, Schaffer J (1993) Real-coded genetic algorithms and interval-schemata. In: Whitley LD (ed) Foundations of genetic algorithms 2. Morgan Kaufmann, San Mateo, pp 187–202
35.
Zurück zum Zitat Fernandes C, Rosa A (2008) Self-adjusting the intensity of assortative mating in genetic algorithms. Soft Comput 12(10):955–979 Fernandes C, Rosa A (2008) Self-adjusting the intensity of assortative mating in genetic algorithms. Soft Comput 12(10):955–979
36.
Zurück zum Zitat Fogarty TC (1989) Varying the probability of mutation in the genetic algorithm. In: Proceedings of the third international conference on genetic algorithms, pp 104–109 Fogarty TC (1989) Varying the probability of mutation in the genetic algorithm. In: Proceedings of the third international conference on genetic algorithms, pp 104–109
37.
Zurück zum Zitat Fogel D (1998) Evolutionary computation: the fossil record. IEEE Press, New York Fogel D (1998) Evolutionary computation: the fossil record. IEEE Press, New York
38.
Zurück zum Zitat Gao J, Sun L, Gen M (2008) A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput Oper Res 35(9): 2892–2907 Gao J, Sun L, Gen M (2008) A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Comput Oper Res 35(9): 2892–2907
39.
Zurück zum Zitat García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez A (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185:1088–1113 García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez A (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185:1088–1113
40.
Zurück zum Zitat García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez A (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113 García-Martínez C, Lozano M, Herrera F, Molina D, Sánchez A (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113
41.
Zurück zum Zitat García-Martínez C, Lozano M, Rodríguez-Díaz F (2012) A simulated annealing method based on a specialised evolutionary algorithm. Appl Soft Comput 12(2):573–588 García-Martínez C, Lozano M, Rodríguez-Díaz F (2012) A simulated annealing method based on a specialised evolutionary algorithm. Appl Soft Comput 12(2):573–588
42.
Zurück zum Zitat Ghannadian F, Alford C, Shonkwiler R (1996) Application of random restart to genetic algorithms. Inf Sci 95(1–2):81–102 Ghannadian F, Alford C, Shonkwiler R (1996) Application of random restart to genetic algorithms. Inf Sci 95(1–2):81–102
43.
Zurück zum Zitat Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York
44.
Zurück zum Zitat Goldberg D (1989) Sizing populations for serial and parallel genetic algorithms. In: Schaffer J (ed) International conference on genetic algorithms. Morgan Kaufmann, pp 70–79 Goldberg D (1989) Sizing populations for serial and parallel genetic algorithms. In: Schaffer J (ed) International conference on genetic algorithms. Morgan Kaufmann, pp 70–79
45.
Zurück zum Zitat Goldberg D, Lingle R (1985) Alleles, Loci and the traveling salesman problem. In: Proceedings of the international conference on genetic algorithms, pp 154–159 Goldberg D, Lingle R (1985) Alleles, Loci and the traveling salesman problem. In: Proceedings of the international conference on genetic algorithms, pp 154–159
46.
Zurück zum Zitat Goldberg D, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette J (ed) Proceedings of the international conference on genetic algorithms. L. Erlbraum Associates, pp 41–49 Goldberg D, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Grefenstette J (ed) Proceedings of the international conference on genetic algorithms. L. Erlbraum Associates, pp 41–49
47.
Zurück zum Zitat Goldberg D, Korb B, Deb K (1990) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530 Goldberg D, Korb B, Deb K (1990) Messy genetic algorithms: motivation, analysis, and first results. Complex Syst 3:493–530
48.
Zurück zum Zitat Gonçalves JF, Resende MG (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17(5):487–525 Gonçalves JF, Resende MG (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17(5):487–525
49.
Zurück zum Zitat Grosan C, Abraham A (2007) Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Grosan C, Abraham A, Ishibuchi H (eds) Hybrid evolutionary algorithms. Springer, Berlin/New York, pp 1–17 Grosan C, Abraham A (2007) Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Grosan C, Abraham A, Ishibuchi H (eds) Hybrid evolutionary algorithms. Springer, Berlin/New York, pp 1–17
50.
Zurück zum Zitat Grötschel M, Padberg MM (1978) On the symmetric traveling salesman problem: theory and computations. In: Optimization and operations research. Lecture notes in econocmics and mathematical systems, vol 157. Springer, pp 105–115 Grötschel M, Padberg MM (1978) On the symmetric traveling salesman problem: theory and computations. In: Optimization and operations research. Lecture notes in econocmics and mathematical systems, vol 157. Springer, pp 105–115
51.
Zurück zum Zitat Gupta S, Garg ML (2013) Binary trie coding scheme: an intelligent genetic algorithm avoiding premature convergence. Int J Comput Math 90(5):881–902 Gupta S, Garg ML (2013) Binary trie coding scheme: an intelligent genetic algorithm avoiding premature convergence. Int J Comput Math 90(5):881–902
52.
Zurück zum Zitat Harik G (1995) Finding multimodal solutions using restricted tournament selection. In: Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, pp 24–31 Harik G (1995) Finding multimodal solutions using restricted tournament selection. In: Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, pp 24–31
53.
Zurück zum Zitat Herrera F, Lozano M (2000) Gradual distributed real-coded genetic algorithms. IEEE Trans Evol Comput 4(1):43–63 Herrera F, Lozano M (2000) Gradual distributed real-coded genetic algorithms. IEEE Trans Evol Comput 4(1):43–63
54.
Zurück zum Zitat Herrera F, Lozano M (2003) Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Comput 7(8):545–562 Herrera F, Lozano M (2003) Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Comput 7(8):545–562
55.
Zurück zum Zitat Herrera F, Lozano M, Verdegay J (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12:265–319 Herrera F, Lozano M, Verdegay J (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12:265–319
56.
Zurück zum Zitat Herrera F, Lozano M, Sánchez A (2003) A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study. Int J Intell Syst 18(3):309–338 Herrera F, Lozano M, Sánchez A (2003) A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study. Int J Intell Syst 18(3):309–338
57.
Zurück zum Zitat Hinterding R, Michalewicz Z, Eiben A (1997) Adaptation in evolutionary computation: a survey. In: IEEE international conference on evolutionary computation, pp 65–69 Hinterding R, Michalewicz Z, Eiben A (1997) Adaptation in evolutionary computation: a survey. In: IEEE international conference on evolutionary computation, pp 65–69
58.
Zurück zum Zitat Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor
59.
Zurück zum Zitat Hutter M, Legg S (2006) Fitness uniform optimization. IEEE Trans Evol Comput 10(5): 568–589 Hutter M, Legg S (2006) Fitness uniform optimization. IEEE Trans Evol Comput 10(5): 568–589
60.
Zurück zum Zitat Iman R, Conover W (1982) A distribution-free approach to inducing rank correlation among input variables. Commun Stat Simul Comput 11(3):311–334 Iman R, Conover W (1982) A distribution-free approach to inducing rank correlation among input variables. Commun Stat Simul Comput 11(3):311–334
61.
Zurück zum Zitat Janikow C, Michalewicz Z (1991) An experimental comparison of binary and floating point representation in genetic algorithms. In: Proceedings of the fourth international conference on genetic algorithms, pp 31–36 Janikow C, Michalewicz Z (1991) An experimental comparison of binary and floating point representation in genetic algorithms. In: Proceedings of the fourth international conference on genetic algorithms, pp 31–36
62.
Zurück zum Zitat Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317 Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317
63.
Zurück zum Zitat Karafotias G, Hoogendoorn M, Eiben A (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187 Karafotias G, Hoogendoorn M, Eiben A (2015) Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans Evol Comput 19(2):167–187
64.
Zurück zum Zitat Kazarlis S, Papadakis S, Theocharis J, Petridis V (2001) Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans Evol Comput 5(3):204–217 Kazarlis S, Papadakis S, Theocharis J, Petridis V (2001) Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans Evol Comput 5(3):204–217
65.
Zurück zum Zitat Kazimipour B, Li X, Qin A (2014) A review of population initialization techniques for evolutionary algorithms. In: Proceedings of the IEEE congress on evolutionary computation, pp 2585–2592 Kazimipour B, Li X, Qin A (2014) A review of population initialization techniques for evolutionary algorithms. In: Proceedings of the IEEE congress on evolutionary computation, pp 2585–2592
66.
Zurück zum Zitat Kita H (2001) A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms. Evol Comput 9(2):223–241 Kita H (2001) A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms. Evol Comput 9(2):223–241
67.
Zurück zum Zitat Kominami M, Hamagami T (2007) A new genetic algorithm with diploid chromosomes by using probability decoding for non-stationary function optimization. In: IEEE international conference on systems, man and cybernetics, 2007. ISIC. pp 1268–1273 Kominami M, Hamagami T (2007) A new genetic algorithm with diploid chromosomes by using probability decoding for non-stationary function optimization. In: IEEE international conference on systems, man and cybernetics, 2007. ISIC. pp 1268–1273
68.
Zurück zum Zitat Koumousis V, Katsaras C (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 Koumousis V, Katsaras C (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
69.
Zurück zum Zitat Krishnakumar K (1989) Micro-genetic algorithms for stationary and non-stationary function optimization. In: Intelligent control and adaptive systems. Proceedings of the SPIE, vol 1196, pp 289–296 Krishnakumar K (1989) Micro-genetic algorithms for stationary and non-stationary function optimization. In: Intelligent control and adaptive systems. Proceedings of the SPIE, vol 1196, pp 289–296
70.
Zurück zum Zitat Kuo T, Hwang S (1996) A genetic algorithm with disruptive selection. IEEE Trans Syst Man Cybern 26(2):299–307 Kuo T, Hwang S (1996) A genetic algorithm with disruptive selection. IEEE Trans Syst Man Cybern 26(2):299–307
71.
Zurück zum Zitat Kurahashi S, Terano T (2000) A genetic algorithm with tabu search for multimodal and multiobjective function optimization. In: Whitley LD, Goldberg DE, Cant-Paz E, Spector L, Parmee IC, Beyer HG (eds) GECCO. Morgan Kaufmann, pp 291–298 Kurahashi S, Terano T (2000) A genetic algorithm with tabu search for multimodal and multiobjective function optimization. In: Whitley LD, Goldberg DE, Cant-Paz E, Spector L, Parmee IC, Beyer HG (eds) GECCO. Morgan Kaufmann, pp 291–298
72.
Zurück zum Zitat Larrañaga P, Kuijpers C, Murga R, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13(2):129–170 Larrañaga P, Kuijpers C, Murga R, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13(2):129–170
73.
Zurück zum Zitat Li JP, Balazs ME, Parks GT, Clarkson PJ (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3):207–234 Li JP, Balazs ME, Parks GT, Clarkson PJ (2002) A species conserving genetic algorithm for multimodal function optimization. Evol Comput 10(3):207–234
74.
Zurück zum Zitat Liang Y, Leung KS (2011) Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput 11(2):2017–2034 Liang Y, Leung KS (2011) Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput 11(2):2017–2034
75.
Zurück zum Zitat Lozano M, García-Martínez C (2010) Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput Oper Res 37:481–497 Lozano M, García-Martínez C (2010) Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput Oper Res 37:481–497
76.
Zurück zum Zitat Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12(3):273–302 Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput 12(3):273–302
77.
Zurück zum Zitat Lozano M, Herrera F, Cano JR (2008) Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Inf Sci 178(23):4421–4433 Lozano M, Herrera F, Cano JR (2008) Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Inf Sci 178(23):4421–4433
78.
Zurück zum Zitat Mahfoud S (1992) Crowding and preselection revised. In: Männer R, Manderick B (eds) Parallel problem solving from nature, vol 2. Elsevier Science, pp 27–36 Mahfoud S (1992) Crowding and preselection revised. In: Männer R, Manderick B (eds) Parallel problem solving from nature, vol 2. Elsevier Science, pp 27–36
79.
Zurück zum Zitat Mallipeddi R, Suganthan P (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14(4):561–579 Mallipeddi R, Suganthan P (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14(4):561–579
80.
Zurück zum Zitat Mauldin M (1984) Maintaining diversity in genetic search. In: National conference on artificial intelligence, Austin, pp 247–250 Mauldin M (1984) Maintaining diversity in genetic search. In: National conference on artificial intelligence, Austin, pp 247–250
81.
Zurück zum Zitat Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer, Berlin/New York Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer, Berlin/New York
82.
Zurück zum Zitat Michalewicz A, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32 Michalewicz A, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evol Comput 4(1):1–32
83.
Zurück zum Zitat Molina D, Lozano M, García-Martínez C, Herrera F (2010) Memetic algorithms for continuous optimization based on local search chains. Evol Comput 18(1):27–63 Molina D, Lozano M, García-Martínez C, Herrera F (2010) Memetic algorithms for continuous optimization based on local search chains. Evol Comput 18(1):27–63
84.
Zurück zum Zitat Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer Academic, Boston, pp 105–144 Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms. In: Glover F, Kochenberger GA (eds) Handbook of metaheuristics. Kluwer Academic, Boston, pp 105–144
85.
Zurück zum Zitat Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125 Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125
86.
Zurück zum Zitat Nomura T, Shimohara K (2001) An analysis of two-parent recombinations for real-valued chromosomes in an infinite population. Evol Comput 9(3):283–308 Nomura T, Shimohara K (2001) An analysis of two-parent recombinations for real-valued chromosomes in an infinite population. Evol Comput 9(3):283–308
87.
Zurück zum Zitat Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437 Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437
88.
Zurück zum Zitat Oliver I, Smith D, Holland J (1987) A study of permutation crossover operators on the TSP. In: Proceedings of the international conference on genetic algorithms and their applications, pp 224–230 Oliver I, Smith D, Holland J (1987) A study of permutation crossover operators on the TSP. In: Proceedings of the international conference on genetic algorithms and their applications, pp 224–230
89.
Zurück zum Zitat Pereira A, de Andrade BB (2015) On the genetic algorithm with adaptive mutation rate and selected statistical applications. Comput Stat 30(1):131–150 Pereira A, de Andrade BB (2015) On the genetic algorithm with adaptive mutation rate and selected statistical applications. Comput Stat 30(1):131–150
90.
Zurück zum Zitat Potts J, Giddens T, Yadav S (1994) The development and evaluation of an improved genetic algorithm based on migration and artificial selection. IEEE Trans Syst Man Cybern 24: 73–86 Potts J, Giddens T, Yadav S (1994) The development and evaluation of an improved genetic algorithm based on migration and artificial selection. IEEE Trans Syst Man Cybern 24: 73–86
91.
Zurück zum Zitat Preechakul C, Kheawhom S (2009) Modified genetic algorithm with sampling techniques for chemical engineering optimization. J Ind Eng Chem 15:110–118 Preechakul C, Kheawhom S (2009) Modified genetic algorithm with sampling techniques for chemical engineering optimization. J Ind Eng Chem 15:110–118
92.
Zurück zum Zitat Preux P, Talbi E (1999) Towards hybrid evolutionary algorithms. Int Trans Oper Res 6(6): 557–570 Preux P, Talbi E (1999) Towards hybrid evolutionary algorithms. Int Trans Oper Res 6(6): 557–570
93.
Zurück zum Zitat Raidl G (2006) A unified view on hybrid metaheuristics. In: Almeida F, Aguilera MB, Blum C, Vega JM, Pérez MP, Roli A, Sampels M (eds) Hybrid metaheuristics, LNCS, vol 4030. Springer, pp 1–12 Raidl G (2006) A unified view on hybrid metaheuristics. In: Almeida F, Aguilera MB, Blum C, Vega JM, Pérez MP, Roli A, Sampels M (eds) Hybrid metaheuristics, LNCS, vol 4030. Springer, pp 1–12
94.
Zurück zum Zitat Reeves C (2010) Genetic algorithms. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics, vol 146. Springer, New York, pp 109–139 Reeves C (2010) Genetic algorithms. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics, vol 146. Springer, New York, pp 109–139
95.
Zurück zum Zitat Reeves C, Rowe J (2001) Genetic algorithms: principles and perspectives. Kluwer, Norwell Reeves C, Rowe J (2001) Genetic algorithms: principles and perspectives. Kluwer, Norwell
96.
Zurück zum Zitat Rodriguez F, Garcia-Martinez C, Lozano M (2012) Hybrid metaheuristics based on evolutionary algorithms and simulated annealing: taxonomy, comparison, and synergy test. IEEE Trans Evol Comput 16(6):787–800 Rodriguez F, Garcia-Martinez C, Lozano M (2012) Hybrid metaheuristics based on evolutionary algorithms and simulated annealing: taxonomy, comparison, and synergy test. IEEE Trans Evol Comput 16(6):787–800
97.
Zurück zum Zitat Sareni B, Krahenbuhl L (1998) Fitness sharing and Niching methods revisited. IEEE Trans Evol Comput 2(3):97–106 Sareni B, Krahenbuhl L (1998) Fitness sharing and Niching methods revisited. IEEE Trans Evol Comput 2(3):97–106
98.
Zurück zum Zitat Serpell M, Smith J (2010) Self-adaption of mutation operator and probability for permutation representations in genetic algorithms. Evol Comput 18(3):491–514 Serpell M, Smith J (2010) Self-adaption of mutation operator and probability for permutation representations in genetic algorithms. Evol Comput 18(3):491–514
99.
Zurück zum Zitat Smith JE, Fogarty TC (1997) Operator and parameter adaptation in genetic algorithms. Soft Comput 1(2):81–87 Smith JE, Fogarty TC (1997) Operator and parameter adaptation in genetic algorithms. Soft Comput 1(2):81–87
100.
Zurück zum Zitat Smith A, Coit D, Baeck T, Fogel D, Michalewicz Z (1997) Penalty functions. In: Bäck T, Fogel DB, Michalewics Z (eds) Handbook on evolutionary computation. Oxford University Press, New York, pp C5.2:1–C5.2:6 Smith A, Coit D, Baeck T, Fogel D, Michalewicz Z (1997) Penalty functions. In: Bäck T, Fogel DB, Michalewics Z (eds) Handbook on evolutionary computation. Oxford University Press, New York, pp C5.2:1–C5.2:6
101.
Zurück zum Zitat Srinivas M, Patnaik L (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667 Srinivas M, Patnaik L (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667
102.
Zurück zum Zitat Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the international conference on genetic algorithms, pp 2–9 Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the international conference on genetic algorithms, pp 2–9
103.
Zurück zum Zitat Talbi E (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564 Talbi E (2002) A taxonomy of hybrid metaheuristics. J Heuristics 8(5):541–564
104.
Zurück zum Zitat Talbi EG, Bachelet V (2006) Cosearch: a parallel cooperative metaheuristic. J Math Model Algorithms 5(1):5–22 Talbi EG, Bachelet V (2006) Cosearch: a parallel cooperative metaheuristic. J Math Model Algorithms 5(1):5–22
105.
Zurück zum Zitat Tantar A, Melab N, Talbi E (2008) A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Comput 12(12):1185–1198 Tantar A, Melab N, Talbi E (2008) A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction. Soft Comput 12(12):1185–1198
106.
Zurück zum Zitat Thierens D (1998) Selection schemes, elitist recombination, and selection intensity. In: Proceedings of the 7th international conference on genetic algorithms. Morgan Kaufmann, pp 152–159 Thierens D (1998) Selection schemes, elitist recombination, and selection intensity. In: Proceedings of the 7th international conference on genetic algorithms. Morgan Kaufmann, pp 152–159
107.
Zurück zum Zitat Ting CK, Li ST, Lee C (2003) On the harmonious mating strategy through tabu search. Inf Sci 156:189–214 Ting CK, Li ST, Lee C (2003) On the harmonious mating strategy through tabu search. Inf Sci 156:189–214
108.
Zurück zum Zitat Tuson A, Ross P (1998) Adapting operator settings in genetic algorithms. Evol Comput 6(2):161–184 Tuson A, Ross P (1998) Adapting operator settings in genetic algorithms. Evol Comput 6(2):161–184
109.
Zurück zum Zitat Uyar Ai, Harmanci AE (2005) A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput 9(11): 803–814 Uyar Ai, Harmanci AE (2005) A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput 9(11): 803–814
110.
Zurück zum Zitat van Kemenade C, Kok J, Eiben AE (1995) Raising GA performance by simultaneous tuning of selective pressure and recombination disruptiveness. In: Proceedings of the 1995 IEEE congress on evolutionary computation (CEC 1995), pp 346–351 van Kemenade C, Kok J, Eiben AE (1995) Raising GA performance by simultaneous tuning of selective pressure and recombination disruptiveness. In: Proceedings of the 1995 IEEE congress on evolutionary computation (CEC 1995), pp 346–351
111.
Zurück zum Zitat Venkatraman S, Yen G (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9(4):424–435 Venkatraman S, Yen G (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9(4):424–435
112.
Zurück zum Zitat Vrugt J, Robinson B, Hyman J (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13(2):243–259 Vrugt J, Robinson B, Hyman J (2009) Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans Evol Comput 13(2):243–259
113.
Zurück zum Zitat Whitley D (1989) The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In: Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, pp 116–121 Whitley D (1989) The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best. In: Proceedings of the international conference on genetic algorithms. Morgan Kaufmann, pp 116–121
114.
Zurück zum Zitat Wong YY, Lee KH, Leung KS, Ho CW (2003) A novel approach in parameter adaptation and diversity maintenance for genetic algorithms. Soft Comput 7(8):506–515 Wong YY, Lee KH, Leung KS, Ho CW (2003) A novel approach in parameter adaptation and diversity maintenance for genetic algorithms. Soft Comput 7(8):506–515
115.
Zurück zum Zitat Yang CH, Nygard K (1993) Effects of initial population in genetic search for time constrained traveling salesman problems. In: Proceedings of the ACM computer science conference, pp 378–383 Yang CH, Nygard K (1993) Effects of initial population in genetic search for time constrained traveling salesman problems. In: Proceedings of the ACM computer science conference, pp 378–383
116.
Zurück zum Zitat Yang S, Ong Y, Jin Y (eds) (2007) Evolutionary computation in dynamic and uncertain environments. Studies in computational intelligence, vol 51. Springer, Berlin/London Yang S, Ong Y, Jin Y (eds) (2007) Evolutionary computation in dynamic and uncertain environments. Studies in computational intelligence, vol 51. Springer, Berlin/London
117.
Zurück zum Zitat Yao J, Kharma N, Grogono P (2010) Bi-objective multipopulation genetic algorithm for multimodal function optimization. IEEE Trans Evol Comput 14(1):80–102 CrossRef Yao J, Kharma N, Grogono P (2010) Bi-objective multipopulation genetic algorithm for multimodal function optimization. IEEE Trans Evol Comput 14(1):80–102 CrossRef
118.
Zurück zum Zitat Yeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10(1):45–56 MathSciNet Yeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10(1):45–56 MathSciNet
119.
Zurück zum Zitat Yuen SY, Chow CK (2009) A genetic algorithm that adaptively mutates and never revisits. IEEE Trans Evol Comput 13(2):454–472 CrossRef Yuen SY, Chow CK (2009) A genetic algorithm that adaptively mutates and never revisits. IEEE Trans Evol Comput 13(2):454–472 CrossRef
Metadaten
Titel
Genetic Algorithms
verfasst von
Carlos García-Martínez
Francisco J. Rodriguez
Manuel Lozano
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-07124-4_28

Premium Partner