Skip to main content
Erschienen in:
Buchtitelbild

2019 | OriginalPaper | Buchkapitel

1. Evolutionary Computation

verfasst von : Jing Liu, Hussein A. Abbass, Kay Chen Tan

Erschienen in: Evolutionary Computation and Complex Networks

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Evolutionary algorithms (EAs) are optimization heuristics designed to solve optimization problems. This chapter introduces classical EAs and other advanced methods including differential evolution, memetic algorithms, particle swarm optimization, and multi-objective EAs.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Fußnoten
1
We will not differentiate between objective and fitness functions in parameter optimization problems in this book.
 
Literatur
1.
Zurück zum Zitat Abbass, H.: The self-adaptive pareto differential evolution algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2002), vol. 1, pp. 831–836. IEEE Press, Piscataway, NJ (2002) Abbass, H.: The self-adaptive pareto differential evolution algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2002), vol. 1, pp. 831–836. IEEE Press, Piscataway, NJ (2002)
2.
Zurück zum Zitat Abbass, H., Sarker, R., Newton, C.: PDE: A pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2001), vol. 2, pp. 971–978. IEEE Press, Piscataway, NJ (2001) Abbass, H., Sarker, R., Newton, C.: PDE: A pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2001), vol. 2, pp. 971–978. IEEE Press, Piscataway, NJ (2001)
3.
Zurück zum Zitat Abbass, H.A.: Mbo: marriage in honey bees optimization-a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 207–214. IEEE (2001) Abbass, H.A.: Mbo: marriage in honey bees optimization-a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 207–214. IEEE (2001)
4.
Zurück zum Zitat Abbass, H.A.: An agent based approach to 3-SAT using marriage in honey-bees optimization. Int. J. Know. Based Intell. Eng. Syst. 6(2), 64–71 (2002) Abbass, H.A.: An agent based approach to 3-SAT using marriage in honey-bees optimization. Int. J. Know. Based Intell. Eng. Syst. 6(2), 64–71 (2002)
5.
Zurück zum Zitat Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med. 25(3), 265–281 (2002)CrossRef Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med. 25(3), 265–281 (2002)CrossRef
6.
Zurück zum Zitat Abbass, H.A., Sarker, R.: The pareto differential evolution algorithm. Int. J. Artif. Intell. Tools 11(04), 531–552 (2002)CrossRef Abbass, H.A., Sarker, R.: The pareto differential evolution algorithm. Int. J. Artif. Intell. Tools 11(04), 531–552 (2002)CrossRef
7.
Zurück zum Zitat Abbass, H.A., Sarker, R., Newton, C.: PDE: a pareto frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 971–978. IEEE Service Center, Seoul Korea (2001) Abbass, H.A., Sarker, R., Newton, C.: PDE: a pareto frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 971–978. IEEE Service Center, Seoul Korea (2001)
8.
Zurück zum Zitat Bagley, J.D.: The behavior of adaptive system which employ genetic and correlation algorithm. Ph.D. thesis, University of Michigan (1967) Bagley, J.D.: The behavior of adaptive system which employ genetic and correlation algorithm. Ph.D. thesis, University of Michigan (1967)
9.
Zurück zum Zitat Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multi-objective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)CrossRef Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multi-objective optimization algorithm: AMOSA. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)CrossRef
10.
Zurück zum Zitat Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(1), 28–41 (2007)CrossRef Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37(1), 28–41 (2007)CrossRef
11.
Zurück zum Zitat Cavicchio, D.J.: Adaptive search using simulated evolution. Ph.D. thesis, University of Michigan (1970) Cavicchio, D.J.: Adaptive search using simulated evolution. Ph.D. thesis, University of Michigan (1970)
12.
Zurück zum Zitat Chen, M., Ludwig, S.A.: Discrete particle swarm optimization with local search strategy for rule classification. In: 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 162–167. IEEE (2012) Chen, M., Ludwig, S.A.: Discrete particle swarm optimization with local search strategy for rule classification. In: 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 162–167. IEEE (2012)
13.
Zurück zum Zitat Chuang, L.Y., Tsai, S.W., Yang, C.H.: Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Appl. Math. Comput. 217(16), 6900–6916 (2011)MathSciNetMATH Chuang, L.Y., Tsai, S.W., Yang, C.H.: Chaotic catfish particle swarm optimization for solving global numerical optimization problems. Appl. Math. Comput. 217(16), 6900–6916 (2011)MathSciNetMATH
14.
Zurück zum Zitat Coello, C.A.C., Pulido, G.T., et al.: A micro-genetic algorithm for multi-objective optimization. In: EMO, vol. 1, pp. 126–140. Springer (2001) Coello, C.A.C., Pulido, G.T., et al.: A micro-genetic algorithm for multi-objective optimization. In: EMO, vol. 1, pp. 126–140. Springer (2001)
15.
Zurück zum Zitat Coello Coello, C.A.: Mopso: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1051–1056 (2002) Coello Coello, C.A.: Mopso: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 2, pp. 1051–1056 (2002)
16.
Zurück zum Zitat Corne, D.W., Knowles, J.D., Oates, M.J.: The pareto envelope-based selection algorithm for multi-objective optimization. In: International Conference on Parallel Problem Solving from Nature, pp. 839–848. Springer (2000) Corne, D.W., Knowles, J.D., Oates, M.J.: The pareto envelope-based selection algorithm for multi-objective optimization. In: International Conference on Parallel Problem Solving from Nature, pp. 839–848. Springer (2000)
17.
Zurück zum Zitat Daneshyari, M., Yen, G.G.: Constrained multiple-swarm particle swarm optimization within a cultural framework. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42(2), 475–490 (2012)CrossRef Daneshyari, M., Yen, G.G.: Constrained multiple-swarm particle swarm optimization within a cultural framework. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42(2), 475–490 (2012)CrossRef
18.
Zurück zum Zitat Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)CrossRef Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)CrossRef
19.
Zurück zum Zitat Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRef Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRef
20.
Zurück zum Zitat Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRef
21.
Zurück zum Zitat DeJong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis (1975) DeJong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis (1975)
22.
Zurück zum Zitat Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992) Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)
23.
Zurück zum Zitat Fan, H.Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. J. Glob. Optim. 27(1), 105–129 (2003)MathSciNetCrossRef Fan, H.Y., Lampinen, J.: A trigonometric mutation operation to differential evolution. J. Glob. Optim. 27(1), 105–129 (2003)MathSciNetCrossRef
24.
Zurück zum Zitat Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through simulated evolution (1966) Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through simulated evolution (1966)
25.
Zurück zum Zitat Fonseca, C.M., Fleming, P.J., et al.: Genetic algorithms for multi-objective optimization: formulation discussion and generalization. In: Icga, vol. 93, pp. 416–423 (1993) Fonseca, C.M., Fleming, P.J., et al.: Genetic algorithms for multi-objective optimization: formulation discussion and generalization. In: Icga, vol. 93, pp. 416–423 (1993)
26.
Zurück zum Zitat Goldberg, D.: Genetic algorithms in search, optimization, and machine learning (1989) Goldberg, D.: Genetic algorithms in search, optimization, and machine learning (1989)
27.
Zurück zum Zitat Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989) Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)
28.
Zurück zum Zitat Goldberg, D.E., Richardson, J., et al.: Genetic algorithms with sharing for multimodal function optimization. In: Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49. Lawrence Erlbaum, Hillsdale, NJ (1987) Goldberg, D.E., Richardson, J., et al.: Genetic algorithms with sharing for multimodal function optimization. In: Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, pp. 41–49. Lawrence Erlbaum, Hillsdale, NJ (1987)
29.
Zurück zum Zitat Gravel, M., Price, W.L., Gagné, C.: Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. Eur. J. Oper. Res. 143(1), 218–229 (2002)CrossRef Gravel, M., Price, W.L., Gagné, C.: Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. Eur. J. Oper. Res. 143(1), 218–229 (2002)CrossRef
30.
Zurück zum Zitat Gwee, B.H., Lim, M.H.: A GA with heuristic-based decoder for ic floorplanning. Integr. VLSI J. 28(2), 157–172 (1999)CrossRef Gwee, B.H., Lim, M.H.: A GA with heuristic-based decoder for ic floorplanning. Integr. VLSI J. 28(2), 157–172 (1999)CrossRef
31.
Zurück zum Zitat Hansen, M.P.: Tabu search for multi-objective optimization: MOTS. In: Proceedings of the 13th International Conference on Multiple Criteria Decision Making, pp. 574–586 (1997) Hansen, M.P.: Tabu search for multi-objective optimization: MOTS. In: Proceedings of the 13th International Conference on Multiple Criteria Decision Making, pp. 574–586 (1997)
32.
Zurück zum Zitat Harp, S.: Towards the genetic synthesis of neural networks. In: ICGA, pp. 360–369 (1989) Harp, S.: Towards the genetic synthesis of neural networks. In: ICGA, pp. 360–369 (1989)
33.
Zurück zum Zitat Hasan, S.K., Sarker, R., Essam, D., Cornforth, D.: Memetic algorithms for solving job-shop scheduling problems. Memet. Comput. 1(1), 69–83 (2009)CrossRef Hasan, S.K., Sarker, R., Essam, D., Cornforth, D.: Memetic algorithms for solving job-shop scheduling problems. Memet. Comput. 1(1), 69–83 (2009)CrossRef
34.
Zurück zum Zitat Holland, J.H.: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Application to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor, MI (1975) Holland, J.H.: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Application to Biology, Control, and Artificial Intelligence. University of Michigan Press, Ann Arbor, MI (1975)
35.
Zurück zum Zitat Hollstien, R.B.: Artificial genetic adaptation in computer control systems. Ph.D. thesis, University of Michigan (1971) Hollstien, R.B.: Artificial genetic adaptation in computer control systems. Ph.D. thesis, University of Michigan (1971)
36.
Zurück zum Zitat Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multi-objective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, pp. 82–87. Ieee (1994) Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multi-objective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, pp. 82–87. Ieee (1994)
37.
Zurück zum Zitat Iorio, A.W., Li, X.: Solving rotated multi-objective optimization problems using differential evolution. In: Australasian Joint Conference on Artificial Intelligence, pp. 861–872. Springer (2004) Iorio, A.W., Li, X.: Solving rotated multi-objective optimization problems using differential evolution. In: Australasian Joint Conference on Artificial Intelligence, pp. 861–872. Springer (2004)
38.
Zurück zum Zitat Jeong, S., Hasegawa, S., Shimoyama, K., Obayashi, S.: Development and investigation of efficient GA/PSO-hybrid algorithm applicable to real-world design optimization. IEEE Comput. Intell. Mag. 4(3) (2009)CrossRef Jeong, S., Hasegawa, S., Shimoyama, K., Obayashi, S.: Development and investigation of efficient GA/PSO-hybrid algorithm applicable to real-world design optimization. IEEE Comput. Intell. Mag. 4(3) (2009)CrossRef
39.
Zurück zum Zitat Kan, W., Jihong, S.: The convergence basis of particle swarm optimization. In: 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE), pp. 63–66. IEEE (2012) Kan, W., Jihong, S.: The convergence basis of particle swarm optimization. In: 2012 International Conference on Industrial Control and Electronics Engineering (ICICEE), pp. 63–66. IEEE (2012)
40.
Zurück zum Zitat Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS 2003, pp. 80–87. IEEE (2003) Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS 2003, pp. 80–87. IEEE (2003)
41.
Zurück zum Zitat Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948. IEEE (1995) Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948. IEEE (1995)
42.
Zurück zum Zitat Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)CrossRef Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)CrossRef
43.
Zurück zum Zitat Knowles, J.D., Corne, D.W.: M-paes: a memetic algorithm for multi-objective optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 325–332. IEEE (2000) Knowles, J.D., Corne, D.W.: M-paes: a memetic algorithm for multi-objective optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1, pp. 325–332. IEEE (2000)
44.
Zurück zum Zitat Krishnanand, K., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE Swarm Intelligence Symposium. SIS 2005, pp. 84–91. IEEE (2005) Krishnanand, K., Ghose, D.: Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE Swarm Intelligence Symposium. SIS 2005, pp. 84–91. IEEE (2005)
45.
Zurück zum Zitat Kumar, S., Chaturvedi, D.: Tuning of particle swarm optimization parameter using fuzzy logic. In: 2011 International Conference on Communication Systems and Network Technologies (CSNT), pp. 174–179. IEEE (2011) Kumar, S., Chaturvedi, D.: Tuning of particle swarm optimization parameter using fuzzy logic. In: 2011 International Conference on Communication Systems and Network Technologies (CSNT), pp. 174–179. IEEE (2011)
46.
Zurück zum Zitat Lim, D., Ong, Y.S., Lim, M.H., Jin, Y.: Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty, pp. 437–456 (2007) Lim, D., Ong, Y.S., Lim, M.H., Jin, Y.: Single/multi-objective inverse robust evolutionary design methodology in the presence of uncertainty, pp. 437–456 (2007)
47.
Zurück zum Zitat Lim, K.K., Ong, Y.S., Lim, M.H., Chen, X., Agarwal, A.: Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput. 12(10), 981–994 (2008)CrossRef Lim, K.K., Ong, Y.S., Lim, M.H., Chen, X., Agarwal, A.: Hybrid ant colony algorithms for path planning in sparse graphs. Soft Comput. 12(10), 981–994 (2008)CrossRef
48.
Zurück zum Zitat Lim, M., Xu, Y.: Application of hybrid genetic algorithm in supply chain management. Int. J. Comput. Syst. Signals. Special issue on Multi-objective Evolution: Theory and Applications 6(1) (2005) Lim, M., Xu, Y.: Application of hybrid genetic algorithm in supply chain management. Int. J. Comput. Syst. Signals. Special issue on Multi-objective Evolution: Theory and Applications 6(1) (2005)
49.
Zurück zum Zitat Lim, M.H., Gustafson, S., Krasnogor, N., Ong, Y.S.: Editorial to the first issue. Memet. Comput. 1, 1–2 (2009)CrossRef Lim, M.H., Gustafson, S., Krasnogor, N., Ong, Y.S.: Editorial to the first issue. Memet. Comput. 1, 1–2 (2009)CrossRef
50.
Zurück zum Zitat Loughlin, D.H., Ranjithan, S.R.: The neighborhood constraint method: a genetic algorithm-based multi-objective optimization technique. In: ICGA, pp. 666–673 (1997) Loughlin, D.H., Ranjithan, S.R.: The neighborhood constraint method: a genetic algorithm-based multi-objective optimization technique. In: ICGA, pp. 666–673 (1997)
51.
Zurück zum Zitat McMullen, P.R.: An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artif. Intell. Eng. 15(3), 309–317 (2001)CrossRef McMullen, P.R.: An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Artif. Intell. Eng. 15(3), 309–317 (2001)CrossRef
52.
Zurück zum Zitat Moscato, P., et al.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P Report, vol. 826 (1989) Moscato, P., et al.: On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P Report, vol. 826 (1989)
53.
Zurück zum Zitat Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (mopso). In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS 2003, pp. 26–33. IEEE (2003) Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (mopso). In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS 2003, pp. 26–33. IEEE (2003)
54.
Zurück zum Zitat Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm I. Continuous parameter optimization. Evol. Comput. 1(1), 25–49 (1993)CrossRef Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm I. Continuous parameter optimization. Evol. Comput. 1(1), 25–49 (1993)CrossRef
55.
Zurück zum Zitat Müller, S., Airaghi, S., Marchetto, J., Koumoutsakos, P.: Optimization algorithms based on a model of bacterial chemotaxis. In: Proceedings of 6th International Conference on Simulation of Adaptive Behavior: From Animals to Animats, SAB 2000 Proc. Suppl. Citeseer (2000) Proceedings supplement Citeseer Müller, S., Airaghi, S., Marchetto, J., Koumoutsakos, P.: Optimization algorithms based on a model of bacterial chemotaxis. In: Proceedings of 6th International Conference on Simulation of Adaptive Behavior: From Animals to Animats, SAB 2000 Proc. Suppl. Citeseer (2000) Proceedings supplement Citeseer
56.
Zurück zum Zitat Ong, Y., Keane, A.: A domain knowledge based search advisor for design problem solving environments. Eng. Appl. Artif. Intell. 15(1), 105–116 (2002)CrossRef Ong, Y., Keane, A.: A domain knowledge based search advisor for design problem solving environments. Eng. Appl. Artif. Intell. 15(1), 105–116 (2002)CrossRef
57.
Zurück zum Zitat Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(1), 141–152 (2006) Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 36(1), 141–152 (2006)
58.
Zurück zum Zitat Ong, Y.S., Nair, P.B., Keane, A.J.: Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA J. 41(4), 687–696 (2003)CrossRef Ong, Y.S., Nair, P.B., Keane, A.J.: Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA J. 41(4), 687–696 (2003)CrossRef
59.
Zurück zum Zitat Ong, Y.S., Nair, P.B., Lum, K.Y.: Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Trans. Evol. Comput. 10(4), 392–404 (2006)CrossRef Ong, Y.S., Nair, P.B., Lum, K.Y.: Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Trans. Evol. Comput. 10(4), 392–404 (2006)CrossRef
60.
Zurück zum Zitat Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)MathSciNetCrossRef Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)MathSciNetCrossRef
61.
Zurück zum Zitat Poloni, C.: Hybrid GA for multi-objective aerodynamic shape optimization. pp. 397–415. Wiley, New York (1995) Poloni, C.: Hybrid GA for multi-objective aerodynamic shape optimization. pp. 397–415. Wiley, New York (1995)
62.
Zurück zum Zitat Price, K.V.: Differential evolution versus the functions of the 2/sup nd/ICEO. In: IEEE International Conference on Evolutionary Computation, pp. 153–157. IEEE (1997) Price, K.V.: Differential evolution versus the functions of the 2/sup nd/ICEO. In: IEEE International Conference on Evolutionary Computation, pp. 153–157. IEEE (1997)
63.
Zurück zum Zitat Price, K.V.: An introduction to differential evolution. New ideas in optimization, pp. 79–108 (1999) Price, K.V.: An introduction to differential evolution. New ideas in optimization, pp. 79–108 (1999)
64.
Zurück zum Zitat Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution A Practical Approach to Global Optimization. Springer (2005) Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution A Practical Approach to Global Optimization. Springer (2005)
65.
Zurück zum Zitat Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRef Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRef
66.
Zurück zum Zitat Qiu, C., Wang, C., Zuo, X.: A novel multi-objective particle swarm optimization with k-means based global best selection strategy. Int. J. Comput. Intell. Syst. 6(5), 822–835 (2013)CrossRef Qiu, C., Wang, C., Zuo, X.: A novel multi-objective particle swarm optimization with k-means based global best selection strategy. Int. J. Comput. Intell. Syst. 6(5), 822–835 (2013)CrossRef
67.
Zurück zum Zitat Dawkins, R.: The Selfish Gene. Oxford University Press (1976) Dawkins, R.: The Selfish Gene. Oxford University Press (1976)
68.
Zurück zum Zitat Robič, T., Filipič, B.: Differential evolution for multi-objective optimization. In: Evolutionary Multi-Criterion Optimization, pp. 520–533. Springer (2005) Robič, T., Filipič, B.: Differential evolution for multi-objective optimization. In: Evolutionary Multi-Criterion Optimization, pp. 520–533. Springer (2005)
69.
Zurück zum Zitat Rosenberg, R.S.: Simulation of genetic populations with biochemical properties. Ph.D. thesis, University of Michigan, Ann Arbor (1967) Rosenberg, R.S.: Simulation of genetic populations with biochemical properties. Ph.D. thesis, University of Michigan, Ann Arbor (1967)
70.
Zurück zum Zitat Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Trans. Neural Netw. 5(1), 96–101 (1994)CrossRef Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Trans. Neural Netw. 5(1), 96–101 (1994)CrossRef
71.
Zurück zum Zitat Sasaki, D., Morikawa, M., Obayashi, S., Nakahashi, K.: Aerodynamic shape optimization of supersonic wings by adaptive range multi-objective genetic algorithms. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 639–652. Springer (2001) Sasaki, D., Morikawa, M., Obayashi, S., Nakahashi, K.: Aerodynamic shape optimization of supersonic wings by adaptive range multi-objective genetic algorithms. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 639–652. Springer (2001)
72.
Zurück zum Zitat Schaffer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. thesis, Vanderbilt University, Nashville, TN (USA) (1984) Schaffer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. thesis, Vanderbilt University, Nashville, TN (USA) (1984)
73.
Zurück zum Zitat Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization 1, 101–106 (2001) Shi, Y., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization 1, 101–106 (2001)
74.
Zurück zum Zitat Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)CrossRef Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)CrossRef
75.
Zurück zum Zitat Stender, J.: Parallel Genetic Algorithms: Theory and Applications, vol. 14. IOS press (1993) Stender, J.: Parallel Genetic Algorithms: Theory and Applications, vol. 14. IOS press (1993)
76.
Zurück zum Zitat Storn, R.: Differential Evolution Research—Trends and Open Questions. Springer (2008) Storn, R.: Differential Evolution Research—Trends and Open Questions. Springer (2008)
77.
Zurück zum Zitat Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley Int. Comput. Sci. Inst. 3 (1995) Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley Int. Comput. Sci. Inst. 3 (1995)
78.
Zurück zum Zitat Storn, R., Price, K.: Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 842–844. IEEE (1996) Storn, R., Price, K.: Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 842–844. IEEE (1996)
79.
Zurück zum Zitat Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRef Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)MathSciNetCrossRef
80.
Zurück zum Zitat Sutton, A.M., Lunacek, M., Whitley, L.D.: Differential evolution and non-separability: using selective pressure to focus search. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1428–1435. ACM (2007) Sutton, A.M., Lunacek, M., Whitley, L.D.: Differential evolution and non-separability: using selective pressure to focus search. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1428–1435. ACM (2007)
81.
Zurück zum Zitat Tang, J., Lim, M.H., Ong, Y.S.: Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput. A Fus. Found. Methodol. Appl. 11(9), 873–888 (2007) Tang, J., Lim, M.H., Ong, Y.S.: Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput. A Fus. Found. Methodol. Appl. 11(9), 873–888 (2007)
82.
Zurück zum Zitat Van Veldhuizen, D.A., Lamont, G.B.: Multi-objective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000)CrossRef Van Veldhuizen, D.A., Lamont, G.B.: Multi-objective evolutionary algorithms: analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000)CrossRef
83.
Zurück zum Zitat Voigt, H.M.: Soft Genetic Operators in Evolutionary Algorithms, pp. 123–141 (1995) Voigt, H.M.: Soft Genetic Operators in Evolutionary Algorithms, pp. 123–141 (1995)
84.
Zurück zum Zitat Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRef Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRef
85.
Zurück zum Zitat Yang, S., Wang, M., et al.: A quantum particle swarm optimization 1, 320–324 (2004) Yang, S., Wang, M., et al.: A quantum particle swarm optimization 1, 320–324 (2004)
86.
Zurück zum Zitat Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74 (2010)CrossRef Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65–74 (2010)CrossRef
87.
Zurück zum Zitat Zhang, Q., Li, H.: Moea/d: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRef Zhang, Q., Li, H.: Moea/d: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRef
88.
Zurück zum Zitat Zhang, W., Jin, Y., Li, X., Zhang, X.: A simple way for parameter selection of standard particle swarm optimization. Artif. Intell. Comput. Intell. 436–443 (2011) Zhang, W., Jin, Y., Li, X., Zhang, X.: A simple way for parameter selection of standard particle swarm optimization. Artif. Intell. Comput. Intell. 436–443 (2011)
89.
Zurück zum Zitat Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., Housheya, O.J.: Artificial intelligence and its applications. Math. Probl. Eng. (2014) Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., Housheya, O.J.: Artificial intelligence and its applications. Math. Probl. Eng. (2014)
90.
Zurück zum Zitat Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. (2015) Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. (2015)
91.
Zurück zum Zitat Zhu, Z., Ong, Y.S., Zurada, J.M.: Identification of full and partial class relevant genes. IEEE/ACM Trans. Comput. Biol. Bioinf. 7(2), 263–277 (2010)CrossRef Zhu, Z., Ong, Y.S., Zurada, J.M.: Identification of full and partial class relevant genes. IEEE/ACM Trans. Comput. Biol. Bioinf. 7(2), 263–277 (2010)CrossRef
92.
Zurück zum Zitat Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: improving the strength pareto evolutionary algorithm for multi-objective optimization. In: Giannakoglou, K., Tsahalis, D., Périaux, J., Papailiou, K., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100. CIMNE, Athens (2001) Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: improving the strength pareto evolutionary algorithm for multi-objective optimization. In: Giannakoglou, K., Tsahalis, D., Périaux, J., Papailiou, K., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100. CIMNE, Athens (2001)
93.
Zurück zum Zitat Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRef Zitzler, E., Thiele, L.: Multi-objective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRef
Metadaten
Titel
Evolutionary Computation
verfasst von
Jing Liu
Hussein A. Abbass
Kay Chen Tan
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-319-60000-0_1

Neuer Inhalt