Skip to main content
Top

2016 | OriginalPaper | Chapter

3. Genetic Algorithms

Authors : Ke-Lin Du, M. N. S. Swamy

Published in: Search and Optimization by Metaheuristics

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Evolutionary algorithms (EAs) are the most influential metaheuristics for optimization. Genetic algorithm (GA) is the most popular form of EA. In this chapter, we first give an introduction to evolutionary computation. A state-of-the-art description of GA is then presented.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

Literature
1.
go back to reference Arabas J, Michalewicz Z, Mulawka J. GAVaPS—a genetic algorithm with varying population size. In: Proceedings of the 1st IEEE international conference on evolutionary computation, Orlando, FL, USA, June 1994. p. 73–78. Arabas J, Michalewicz Z, Mulawka J. GAVaPS—a genetic algorithm with varying population size. In: Proceedings of the 1st IEEE international conference on evolutionary computation, Orlando, FL, USA, June 1994. p. 73–78.
2.
go back to reference Araujo L, Merelo JJ. Diversity through multiculturality: assessing migrant choice policies in an island model. IEEE Trans Evol Comput. 2011;15(4):456–69.CrossRef Araujo L, Merelo JJ. Diversity through multiculturality: assessing migrant choice policies in an island model. IEEE Trans Evol Comput. 2011;15(4):456–69.CrossRef
3.
go back to reference Ballester PJ, Carter JN. An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimisation. In: Proceedings of genetic and evolutionary computation conference (GECCO), Seattle, WA, USA, June 2004. p. 901–913. Ballester PJ, Carter JN. An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimisation. In: Proceedings of genetic and evolutionary computation conference (GECCO), Seattle, WA, USA, June 2004. p. 901–913.
4.
go back to reference Bean J. Genetic algorithms and random keys for sequence and optimization. ORSA J Comput. 1994;6(2):154–60.CrossRefMATH Bean J. Genetic algorithms and random keys for sequence and optimization. ORSA J Comput. 1994;6(2):154–60.CrossRefMATH
5.
go back to reference Beyer H-G, Deb K. On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput. 2001;5(3):250–70.CrossRef Beyer H-G, Deb K. On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans Evol Comput. 2001;5(3):250–70.CrossRef
6.
go back to reference Bhandari D, Pal NR, Pal SK. Directed mutation in genetic algorithms. Inf Sci. 1994;79:251–70.CrossRefMATH Bhandari D, Pal NR, Pal SK. Directed mutation in genetic algorithms. Inf Sci. 1994;79:251–70.CrossRefMATH
7.
go back to reference Burke DS, De Jong KA, Grefenstette JJ, Ramsey CL, Wu AS. Putting more genetics into genetic algorithms. Evol Comput. 1998;6(4):387–410.CrossRef Burke DS, De Jong KA, Grefenstette JJ, Ramsey CL, Wu AS. Putting more genetics into genetic algorithms. Evol Comput. 1998;6(4):387–410.CrossRef
8.
go back to reference Cartwright HM, Harris SP. The application of the genetic algorithm to two-dimensional strings: the source apportionment problem. In: Forrest S, editor, Proceedings of the 5th international conference on genetic algorithms, Urbana-Champaign, IL, USA, June 1993. San Mateo, CA: Morgan Kaufmann; 1993. p. 631. Cartwright HM, Harris SP. The application of the genetic algorithm to two-dimensional strings: the source apportionment problem. In: Forrest S, editor, Proceedings of the 5th international conference on genetic algorithms, Urbana-Champaign, IL, USA, June 1993. San Mateo, CA: Morgan Kaufmann; 1993. p. 631.
9.
go back to reference Cervantes J, Stephens CR. Limitations of existing mutation rate heuristics and how a rank GA overcomes them. IEEE Trans Evol Comput. 2009;13(2):369–97.CrossRef Cervantes J, Stephens CR. Limitations of existing mutation rate heuristics and how a rank GA overcomes them. IEEE Trans Evol Comput. 2009;13(2):369–97.CrossRef
10.
go back to reference Chakraborty UK, Janikow CZ. An analysis of Gray versus binary encoding in genetic search. Inf Sci. 2000;156:253–69.MathSciNetCrossRef Chakraborty UK, Janikow CZ. An analysis of Gray versus binary encoding in genetic search. Inf Sci. 2000;156:253–69.MathSciNetCrossRef
11.
go back to reference Chan TM, Man KF, Kwong S, Tang KS. A jumping gene paradigm for evolutionary multiobjective optimization. IEEE Trans Evol Comput. 2008;12(2):143–59.CrossRef Chan TM, Man KF, Kwong S, Tang KS. A jumping gene paradigm for evolutionary multiobjective optimization. IEEE Trans Evol Comput. 2008;12(2):143–59.CrossRef
12.
go back to reference Chen H, Flann NS, Watson DW. Parallel genetic simulated annealing: a massively parallel SIMD algorithm. IEEE Trans Parallel Distrib Syst. 1998;9(2):126–36.CrossRef Chen H, Flann NS, Watson DW. Parallel genetic simulated annealing: a massively parallel SIMD algorithm. IEEE Trans Parallel Distrib Syst. 1998;9(2):126–36.CrossRef
13.
go back to reference Cherkauer KJ. Genetic search for nearest-neighbor exemplars. In: Proceedings of the 4th midwest artificial intelligence and cognitive science society conference, Utica, IL, USA, 1992. p. 87–91. Cherkauer KJ. Genetic search for nearest-neighbor exemplars. In: Proceedings of the 4th midwest artificial intelligence and cognitive science society conference, Utica, IL, USA, 1992. p. 87–91.
14.
go back to reference Chicano F, Sutton AM, Whitley LD, Alba E. Fitness probability distribution of bit-flip mutation. Evol Comput. 2015;23(2):217–48.CrossRef Chicano F, Sutton AM, Whitley LD, Alba E. Fitness probability distribution of bit-flip mutation. Evol Comput. 2015;23(2):217–48.CrossRef
15.
go back to reference Chuang Y-C, Chen C-T, Hwang C. A real-coded genetic algorithm with a direction-based crossover operator. Inf Sci. 2015;305:320–48.CrossRef Chuang Y-C, Chen C-T, Hwang C. A real-coded genetic algorithm with a direction-based crossover operator. Inf Sci. 2015;305:320–48.CrossRef
16.
go back to reference Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput. 2013;219:8121–44.MathSciNetMATH Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput. 2013;219:8121–44.MathSciNetMATH
17.
go back to reference Davis L. Bit-climbing, representational bias, and test suite design. In: Proceedings of the 4th international conference on genetic algorithms, San Diego, CA, USA, July 1991. San Mateo, CA: Morgan Kaufmann; 1991. p. 18–23. Davis L. Bit-climbing, representational bias, and test suite design. In: Proceedings of the 4th international conference on genetic algorithms, San Diego, CA, USA, July 1991. San Mateo, CA: Morgan Kaufmann; 1991. p. 18–23.
18.
go back to reference Davis L, Grefenstette JJ. Concerning GENESIS and OOGA. In: Davis L, editor. Handbook of genetic algorithms. New York: Van Nostrand Reinhold; 1991. p. 374–377. Davis L, Grefenstette JJ. Concerning GENESIS and OOGA. In: Davis L, editor. Handbook of genetic algorithms. New York: Van Nostrand Reinhold; 1991. p. 374–377.
19.
go back to reference Deb K, Anand A, Joshi D. A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput. 2002;10(4):371–95.CrossRef Deb K, Anand A, Joshi D. A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput. 2002;10(4):371–95.CrossRef
20.
go back to reference De Jong K. An analysis of the behavior of a class of genetic adaptive systems. PhD Thesis, University of Michigan, Ann Arbor, MI, USA, 1975. De Jong K. An analysis of the behavior of a class of genetic adaptive systems. PhD Thesis, University of Michigan, Ann Arbor, MI, USA, 1975.
21.
go back to reference Drugan MM, Thierens D. Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms. Evol Intel. 2010;3(2):79–101.CrossRefMATH Drugan MM, Thierens D. Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms. Evol Intel. 2010;3(2):79–101.CrossRefMATH
22.
go back to reference Ericsson M, Resende MGC, Pardalos PM. A genetic algorithm for the weight setting problem in OSPF routing. J Comb Optim. 2002;6:299–333.MathSciNetCrossRefMATH Ericsson M, Resende MGC, Pardalos PM. A genetic algorithm for the weight setting problem in OSPF routing. J Comb Optim. 2002;6:299–333.MathSciNetCrossRefMATH
23.
go back to reference Eshelman LJ. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins GJE, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmannpp; 1991. p. 265–283. Eshelman LJ. The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins GJE, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmannpp; 1991. p. 265–283.
24.
go back to reference Eshelman LJ, Schaffer JD. Real-coded genetic algorithms and interval-schemata. In: Whitley LD, editor, Foundations of genetic algorithms 2. San Mateo, CA: Morgan Kaufmann; 1993. p. 187–202. Eshelman LJ, Schaffer JD. Real-coded genetic algorithms and interval-schemata. In: Whitley LD, editor, Foundations of genetic algorithms 2. San Mateo, CA: Morgan Kaufmann; 1993. p. 187–202.
25.
go back to reference Fogel L, Owens J, Walsh M. Artificial intelligence through simulated evolution. New York: Wiley; 1966.MATH Fogel L, Owens J, Walsh M. Artificial intelligence through simulated evolution. New York: Wiley; 1966.MATH
26.
go back to reference Fox BR, McMahon MB. Genetic operators for sequencing problems. In: Rawlins GJE, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. p. 284–300. Fox BR, McMahon MB. Genetic operators for sequencing problems. In: Rawlins GJE, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. p. 284–300.
27.
go back to reference Frantz DR. Non-linearities in Genetic Adaptive Search. PhD Thesis, University of Michigan, Ann Arbor, MI, USA, 1972. Frantz DR. Non-linearities in Genetic Adaptive Search. PhD Thesis, University of Michigan, Ann Arbor, MI, USA, 1972.
28.
go back to reference Friedrich T, Hebbinghaus N, Neumann F. Rigorous analyses of simple diversity mechanisms. In: Proceedings of genetic and evolutionary computation conference (GECCO), London, UK, July 2007. p. 1219–1225. Friedrich T, Hebbinghaus N, Neumann F. Rigorous analyses of simple diversity mechanisms. In: Proceedings of genetic and evolutionary computation conference (GECCO), London, UK, July 2007. p. 1219–1225.
29.
go back to reference Galan SF, Mengshoel OJ, Pinter R. A novel mating approach for genetic algorithms. Evol Comput. 2012;21(2):197–229.CrossRef Galan SF, Mengshoel OJ, Pinter R. A novel mating approach for genetic algorithms. Evol Comput. 2012;21(2):197–229.CrossRef
30.
go back to reference Garcia-Martinez C, Lozano M, Herrera F, Molina D, Sanchez AM. Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res. 2008;185:1088–113.CrossRefMATH Garcia-Martinez C, Lozano M, Herrera F, Molina D, Sanchez AM. Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res. 2008;185:1088–113.CrossRefMATH
31.
go back to reference Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading, MA, USA: Addison-Wesley; 1989.MATH Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading, MA, USA: Addison-Wesley; 1989.MATH
32.
go back to reference Goldberg D. A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Syst. 4:4:445–460. Goldberg D. A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Syst. 4:4:445–460.
33.
go back to reference Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins GJE, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. p. 69–93. Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins GJE, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. p. 69–93.
34.
go back to reference Goldberg DE, Deb K, Korb B. Messy genetic algorithms: motivation, analysis, and first results. Complex Syst. 1989;3:493–530.MathSciNetMATH Goldberg DE, Deb K, Korb B. Messy genetic algorithms: motivation, analysis, and first results. Complex Syst. 1989;3:493–530.MathSciNetMATH
35.
go back to reference Goldberg DE, Deb K, Kargupta H, Harik G. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: Proceedings of the 5th international conference on genetic algorithms, Urbana-Champaign, IL, USA, June 1993. p. 56–64. Goldberg DE, Deb K, Kargupta H, Harik G. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: Proceedings of the 5th international conference on genetic algorithms, Urbana-Champaign, IL, USA, June 1993. p. 56–64.
36.
go back to reference Goldman BW, Punch WF. Fast and efficient black box optimization using the parameter-less population pyramid. Evol Comput. 2015;23(2):451–79.CrossRef Goldman BW, Punch WF. Fast and efficient black box optimization using the parameter-less population pyramid. Evol Comput. 2015;23(2):451–79.CrossRef
37.
go back to reference Grefenstette JJ, Gopal R, Rosmaita BJ, Gucht DV. Genetic algorithms for the traveling salesman problem. In: Proceedings of the 1st international conference on genetic algorithms and their applications, Pittsburgh, PA, USA, July 1985. Mahwah, NJ: Lawrence Erlbaum Associates; 1985. p. 160–168. Grefenstette JJ, Gopal R, Rosmaita BJ, Gucht DV. Genetic algorithms for the traveling salesman problem. In: Proceedings of the 1st international conference on genetic algorithms and their applications, Pittsburgh, PA, USA, July 1985. Mahwah, NJ: Lawrence Erlbaum Associates; 1985. p. 160–168.
38.
go back to reference Harvey I. The SAGA cross: the mechanics of crossover for variable-length genetic algorithms. In: Proceedings of the 2nd conference on parallel problem solving from nature (PPSN II), Brussels, Belgium, Sept 1992. Amsterdam, The Netherlands: North Holland; 1992. p. 269–278. Harvey I. The SAGA cross: the mechanics of crossover for variable-length genetic algorithms. In: Proceedings of the 2nd conference on parallel problem solving from nature (PPSN II), Brussels, Belgium, Sept 1992. Amsterdam, The Netherlands: North Holland; 1992. p. 269–278.
39.
go back to reference Harvey I. The microbial genetic algorithm. In: Proceedings of 10th european conference on advances in artificial life: Darwin meets von Neumann, Budapest, Hungary, Sept 2009, Part II, p. 126–133. Harvey I. The microbial genetic algorithm. In: Proceedings of 10th european conference on advances in artificial life: Darwin meets von Neumann, Budapest, Hungary, Sept 2009, Part II, p. 126–133.
40.
go back to reference Herrera F, Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera F, Verdegay JL, editors. Genetic algorithms and soft computing. Berlin: Physica-Verlag; 1996. p. 95–125. Herrera F, Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera F, Verdegay JL, editors. Genetic algorithms and soft computing. Berlin: Physica-Verlag; 1996. p. 95–125.
41.
go back to reference Herrera F, Lozano M. Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Comput. 2003;7:545–62.CrossRef Herrera F, Lozano M. Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Comput. 2003;7:545–62.CrossRef
42.
go back to reference Herrera F, Lozano M, Verdegay JL. Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst. 1997;92(1):21–30.CrossRef Herrera F, Lozano M, Verdegay JL. Fuzzy connectives based crossover operators to model genetic algorithms population diversity. Fuzzy Sets Syst. 1997;92(1):21–30.CrossRef
43.
go back to reference Herrera F, Lozano M, S’anchez AM. A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. Int J Intell Syst. 2003;18:3:309–338. Herrera F, Lozano M, S’anchez AM. A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. Int J Intell Syst. 2003;18:3:309–338.
44.
go back to reference Hesser J, Manner R. Towards an optimal mutation probability for genetic algorithms. In: Proceedings of the 1st workshop on parallel problem solving from nature (PPSN I), Dortmund, Germany, Oct 1990. p. 23–32. Hesser J, Manner R. Towards an optimal mutation probability for genetic algorithms. In: Proceedings of the 1st workshop on parallel problem solving from nature (PPSN I), Dortmund, Germany, Oct 1990. p. 23–32.
45.
go back to reference Hillis WD. Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D. 1990;42:228–34.CrossRef Hillis WD. Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D. 1990;42:228–34.CrossRef
46.
47.
go back to reference Holland J. Adaptation in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press; 1975. Holland J. Adaptation in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press; 1975.
48.
go back to reference Holland JH. Building blocks, cohort genetic algorithms and hyperplane-defined functions. Evol Comput. 2000;8(4):373–91.CrossRef Holland JH. Building blocks, cohort genetic algorithms and hyperplane-defined functions. Evol Comput. 2000;8(4):373–91.CrossRef
49.
go back to reference Hutter M, Legg S. Fitness uniform optimization. IEEE Trans Evol Comput. 2006;10(5):568–89.CrossRef Hutter M, Legg S. Fitness uniform optimization. IEEE Trans Evol Comput. 2006;10(5):568–89.CrossRef
50.
go back to reference Hutt B, Warwick K. Synapsing variable-length crossover: meaningful crossover for variable-length genomes. IEEE Trans Evol Comput. 2007;11(1):118–31.CrossRef Hutt B, Warwick K. Synapsing variable-length crossover: meaningful crossover for variable-length genomes. IEEE Trans Evol Comput. 2007;11(1):118–31.CrossRef
51.
go back to reference Jansen T, De Jong KA, Wegener I. On the choice of the offspring population size in evolutionary algorithms. Evol Comput. 2005;13(4):413–40.CrossRef Jansen T, De Jong KA, Wegener I. On the choice of the offspring population size in evolutionary algorithms. Evol Comput. 2005;13(4):413–40.CrossRef
52.
go back to reference Khatib W, Fleming PJ. The stud GA: a mini revolution? In: Eiben A, Back T, Schoenauer M, Schwefel H, editors. Proceedings of the 5th international conference on parallel problem solving from nature (PPSN V). Amsterdam: The Netherlands; 1998. p. 683–691. Khatib W, Fleming PJ. The stud GA: a mini revolution? In: Eiben A, Back T, Schoenauer M, Schwefel H, editors. Proceedings of the 5th international conference on parallel problem solving from nature (PPSN V). Amsterdam: The Netherlands; 1998. p. 683–691.
53.
go back to reference Knjazew D, Goldberg DE. OMEGA—Ordering messy GA: Solving permutation problems with the fast messy genetic algorithm and random keys. In: Proceedings of genetic and evolutionary computation conference (GECCO), Las Vegas, NV, USA, July 2000. p. 181–188. Knjazew D, Goldberg DE. OMEGA—Ordering messy GA: Solving permutation problems with the fast messy genetic algorithm and random keys. In: Proceedings of genetic and evolutionary computation conference (GECCO), Las Vegas, NV, USA, July 2000. p. 181–188.
54.
go back to reference Koumousis VK, Katsaras CP. A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans Evol Comput. 2006;10(1):19–28.CrossRef Koumousis VK, Katsaras CP. A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans Evol Comput. 2006;10(1):19–28.CrossRef
55.
go back to reference Koza JR. Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press; 1992.MATH Koza JR. Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press; 1992.MATH
56.
go back to reference Laskey KB, Myers JW. Population Markov chain Monte Carlo. Mach Learn. 2003;50:175–96. Laskey KB, Myers JW. Population Markov chain Monte Carlo. Mach Learn. 2003;50:175–96.
57.
go back to reference Lee MA, Takagi H. Dynamic control of genetic algorithms using fuzzy logic techniques. In: Proceedings of the 5th international conference on genetic algorithms (ICGA’93), Urbana, IL, USA, July 1993. p. 76–83. Lee MA, Takagi H. Dynamic control of genetic algorithms using fuzzy logic techniques. In: Proceedings of the 5th international conference on genetic algorithms (ICGA’93), Urbana, IL, USA, July 1993. p. 76–83.
58.
go back to reference Lee CY. Entropy-Boltzmann selection in the genetic algorithms. IEEE Trans Syst Man Cybern Part B. 2003;33(1):138–42.CrossRef Lee CY. Entropy-Boltzmann selection in the genetic algorithms. IEEE Trans Syst Man Cybern Part B. 2003;33(1):138–42.CrossRef
59.
go back to reference Leung FHF, Lam HK, Ling SH, Tam PKS. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Networks. 2003;14(1):79–88.CrossRef Leung FHF, Lam HK, Ling SH, Tam PKS. Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Networks. 2003;14(1):79–88.CrossRef
60.
go back to reference Lobo FG, Lima CF. A review of adaptive population sizing schemes in genetic algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO), Washington, DC, USA, June 2005. p. 228–234. Lobo FG, Lima CF. A review of adaptive population sizing schemes in genetic algorithms. In: Proceedings of genetic and evolutionary computation conference (GECCO), Washington, DC, USA, June 2005. p. 228–234.
61.
go back to reference Mathias K, Whitley LD. Changing representations during search: a comparative study of delta coding. Evol Comput. 1995;2(3):249–78.CrossRef Mathias K, Whitley LD. Changing representations during search: a comparative study of delta coding. Evol Comput. 1995;2(3):249–78.CrossRef
62.
go back to reference Matsui K. New selection method to improve the population diversity in genetic algorithms. In: Proceedings of the 1999 IEEE International conference on systems, man, and cybernetics, Tokyo, Japan, Oct 1999. p. 625–630. Matsui K. New selection method to improve the population diversity in genetic algorithms. In: Proceedings of the 1999 IEEE International conference on systems, man, and cybernetics, Tokyo, Japan, Oct 1999. p. 625–630.
63.
go back to reference Mauldin ML. Maintaining diversity in genetic search. In: Proceedings of the 4th national conference on artificial intelligence (AAAI-84), Austin, TX, USA, Aug 1984. p. 247–250. Mauldin ML. Maintaining diversity in genetic search. In: Proceedings of the 4th national conference on artificial intelligence (AAAI-84), Austin, TX, USA, Aug 1984. p. 247–250.
64.
go back to reference Mills KL, Filliben JJ, Haines AL. Determining relative importance and effective settings for genetic algorithm control parameters. Evol Comput. 2015;23(2):309–42.CrossRef Mills KL, Filliben JJ, Haines AL. Determining relative importance and effective settings for genetic algorithm control parameters. Evol Comput. 2015;23(2):309–42.CrossRef
65.
go back to reference Muhlenbein H. Parallel genetic algorithms, population genetics and combinatorial optimization. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufman; 1989. p. 416–421. Muhlenbein H. Parallel genetic algorithms, population genetics and combinatorial optimization. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufman; 1989. p. 416–421.
66.
go back to reference Muhlenbein H. How genetic algorithms really work: mutation and hill climbing. In: Manner R, Manderick B, editors. Proceedings of the 2nd conference on parallel problem solving from nature (PPSN II), Brussels, Belgium, Sept 1992. Amsterdam, The Netherlands: North Holland; 1992. pp. 15–25. Muhlenbein H. How genetic algorithms really work: mutation and hill climbing. In: Manner R, Manderick B, editors. Proceedings of the 2nd conference on parallel problem solving from nature (PPSN II), Brussels, Belgium, Sept 1992. Amsterdam, The Netherlands: North Holland; 1992. pp. 15–25.
67.
go back to reference Muhlenbein H, Paab G. From recombination of genes to the estimation of distributions. I. Binary parameters. In: Proceedings of the 4th International conference on parallel problem solving from nature (PPSN IV), Berlin, Germany, Sept 1996. p. 178–187. Muhlenbein H, Paab G. From recombination of genes to the estimation of distributions. I. Binary parameters. In: Proceedings of the 4th International conference on parallel problem solving from nature (PPSN IV), Berlin, Germany, Sept 1996. p. 178–187.
68.
go back to reference Muhlenbein H, Schlierkamp-Voosen D. Predictive models for the breeder genetic algorithm: continuous parameter optimization. Evol Comput. 1994;1(4):25–49. Muhlenbein H, Schlierkamp-Voosen D. Predictive models for the breeder genetic algorithm: continuous parameter optimization. Evol Comput. 1994;1(4):25–49.
69.
go back to reference Mulenbein H, Schlierkamp-Voose D. Analysis of selection, mutation and recombination in genetic algorithms. In: Banzhaf W, Eechman FH, editors. Evolution and biocomputation: Evolution and biocomputation, computational models of evolution. Berlin: Springer; 1995. p. 142–68. Mulenbein H, Schlierkamp-Voose D. Analysis of selection, mutation and recombination in genetic algorithms. In: Banzhaf W, Eechman FH, editors. Evolution and biocomputation: Evolution and biocomputation, computational models of evolution. Berlin: Springer; 1995. p. 142–68.
70.
go back to reference Nawa NE, Furuhashi T. Fuzzy systems parameters discovery by bacterial evolutionary algorithms. IEEE Trans Fuzzy Syst. 1999;7:608–16.CrossRef Nawa NE, Furuhashi T. Fuzzy systems parameters discovery by bacterial evolutionary algorithms. IEEE Trans Fuzzy Syst. 1999;7:608–16.CrossRef
71.
go back to reference Palmer CC, Kershenbaum A. An approach to a problem in network design using genetic algorithms. Networks. 1995;26:151–63.CrossRefMATH Palmer CC, Kershenbaum A. An approach to a problem in network design using genetic algorithms. Networks. 1995;26:151–63.CrossRefMATH
72.
go back to reference Paulden T, Smith DK. From the Dandelion code to the Rainbow code: a class of bijective spanning tree representations with linear complexity and bounded locality. IEEE Trans Evol Comput. 2006;10(2):108–23.CrossRef Paulden T, Smith DK. From the Dandelion code to the Rainbow code: a class of bijective spanning tree representations with linear complexity and bounded locality. IEEE Trans Evol Comput. 2006;10(2):108–23.CrossRef
73.
go back to reference Perales-Gravan C, Lahoz-Beltra R. An AM radio receiver designed with a genetic algorithm based on a bacterial conjugation genetic operator. IEEE Trans Evol Comput. 2008;12(2):129–42.CrossRef Perales-Gravan C, Lahoz-Beltra R. An AM radio receiver designed with a genetic algorithm based on a bacterial conjugation genetic operator. IEEE Trans Evol Comput. 2008;12(2):129–42.CrossRef
74.
go back to reference Potter MA, De Jong KA. Cooperative coevolution: an architecture for evolving coadapted subcomponenets. Evol Comput. 2000;8(1):1–29.CrossRef Potter MA, De Jong KA. Cooperative coevolution: an architecture for evolving coadapted subcomponenets. Evol Comput. 2000;8(1):1–29.CrossRef
75.
go back to reference Rechenberg I. Evolutionsstrategie-optimierung technischer systeme nach prinzipien der biologischen information. Freiburg, Germany: Formman Verlag; 1973. Rechenberg I. Evolutionsstrategie-optimierung technischer systeme nach prinzipien der biologischen information. Freiburg, Germany: Formman Verlag; 1973.
76.
go back to reference Ronald E. When selection meets seduction. In: Proceedings of the 6th international conference on genetic algorithms, Pittsburgh, PA, USA, July 1995. p. 167–173. Ronald E. When selection meets seduction. In: Proceedings of the 6th international conference on genetic algorithms, Pittsburgh, PA, USA, July 1995. p. 167–173.
77.
go back to reference Rothlauf F, Goldberg DE, Heinzl A. Network random keys—a tree network representation scheme for genetic and evolutionary algorithms. Evol Comput. 2002;10(1):75–97.CrossRef Rothlauf F, Goldberg DE, Heinzl A. Network random keys—a tree network representation scheme for genetic and evolutionary algorithms. Evol Comput. 2002;10(1):75–97.CrossRef
78.
go back to reference Rudolph G. Convergence analysis of canonical genetic algorithm. IEEE Trans Neural Networks. 1994;5(1):96–101.CrossRef Rudolph G. Convergence analysis of canonical genetic algorithm. IEEE Trans Neural Networks. 1994;5(1):96–101.CrossRef
79.
go back to reference Satoh H, Yamamura M, Kobayashi S. Minimal generation gap model for GAs considering both exploration and exploitation. In: Proceedings of the 4th International conference on soft computing (Iizuka’96): Methodologies for the conception, design, and application of intelligent systems, Iizuka, Fukuoka, Japan, Sept 1996. p. 494–497. Satoh H, Yamamura M, Kobayashi S. Minimal generation gap model for GAs considering both exploration and exploitation. In: Proceedings of the 4th International conference on soft computing (Iizuka’96): Methodologies for the conception, design, and application of intelligent systems, Iizuka, Fukuoka, Japan, Sept 1996. p. 494–497.
80.
go back to reference Schaffer JD, Caruana RA, Eshelman LJ, Das R. A study of control parameters affecting online performance of genetic algorithms for function optimisation. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 70–79. Schaffer JD, Caruana RA, Eshelman LJ, Das R. A study of control parameters affecting online performance of genetic algorithms for function optimisation. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 70–79.
81.
go back to reference Schraudolph NN, Belew RK. Dynamic parameter encoding for genetic algorithms. Mach Learn. 1992;9(1):9–21. Schraudolph NN, Belew RK. Dynamic parameter encoding for genetic algorithms. Mach Learn. 1992;9(1):9–21.
82.
go back to reference Schwefel HP. Numerical optimization of computer models. Chichester: Wiley; 1981.MATH Schwefel HP. Numerical optimization of computer models. Chichester: Wiley; 1981.MATH
83.
go back to reference Sharma SK, Irwin GW. Fuzzy coding of genetic algorithms. IEEE Trans Evol Comput. 2003;7(4):344–55.CrossRef Sharma SK, Irwin GW. Fuzzy coding of genetic algorithms. IEEE Trans Evol Comput. 2003;7(4):344–55.CrossRef
84.
go back to reference Simoes AB, Costa E. Enhancing transposition performance. In: Proceedings of congress on evolutionary computation (CEC), Washington, DC, USA, July 1999. p. 1434–1441. Simoes AB, Costa E. Enhancing transposition performance. In: Proceedings of congress on evolutionary computation (CEC), Washington, DC, USA, July 1999. p. 1434–1441.
85.
go back to reference Smith J, Vavak F. Replacement strategies in steady state genetic algorithms: static environments. In: Banzhaf W, Reeves C, editors. Foundations of genetic algorithms 5. CA: Morgan Kaufmann; 1999. p. 219–233. Smith J, Vavak F. Replacement strategies in steady state genetic algorithms: static environments. In: Banzhaf W, Reeves C, editors. Foundations of genetic algorithms 5. CA: Morgan Kaufmann; 1999. p. 219–233.
86.
go back to reference Sokolov A, Whitley D. Unbiased tournament selection. In: Proceedings of the conference on genetic and evolutionary computation (GECCO), Washington, DC, USA, June 2005. p. 1131–1138. Sokolov A, Whitley D. Unbiased tournament selection. In: Proceedings of the conference on genetic and evolutionary computation (GECCO), Washington, DC, USA, June 2005. p. 1131–1138.
87.
go back to reference Srinivas M, Patnaik LM. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern. 1994;24(4):656–67.CrossRef Srinivas M, Patnaik LM. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern. 1994;24(4):656–67.CrossRef
88.
go back to reference Storn R, Price K. Differential evolution–a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, March 1995. Storn R, Price K. Differential evolution–a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, March 1995.
89.
go back to reference Streifel RJ, Marks RJ II, Reed R, Choi JJ, Healy M. Dynamic fuzzy control of genetic algorithm parameter coding. IEEE Trans Syst Man Cybern Part B. 1999;29(3):426–33.CrossRef Streifel RJ, Marks RJ II, Reed R, Choi JJ, Healy M. Dynamic fuzzy control of genetic algorithm parameter coding. IEEE Trans Syst Man Cybern Part B. 1999;29(3):426–33.CrossRef
90.
go back to reference Syswerda G. Uniform crossover in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Francisco: Morgan Kaufmann; 1989. p. 2–9. Syswerda G. Uniform crossover in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Francisco: Morgan Kaufmann; 1989. p. 2–9.
91.
go back to reference Syswerda G. Simulated crossover in genetic algorithms. In: Whitley LD, editor. Foundations of genetic algorithms 2, San Mateo, CA: Morgan Kaufmann; 1993. p. 239–255. Syswerda G. Simulated crossover in genetic algorithms. In: Whitley LD, editor. Foundations of genetic algorithms 2, San Mateo, CA: Morgan Kaufmann; 1993. p. 239–255.
92.
go back to reference Thompson E, Paulden T, Smith DK. The Dandelion code: a new coding of spanning trees for genetic algorithms. IEEE Trans Evol Comput. 2007;11(1):91–100.CrossRef Thompson E, Paulden T, Smith DK. The Dandelion code: a new coding of spanning trees for genetic algorithms. IEEE Trans Evol Comput. 2007;11(1):91–100.CrossRef
93.
go back to reference Tsutsui S, Yamamura M, Higuchi T. Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the genetic and evolutionary computation conference (GECCO), Orlando, FL, USA, July 1999. San Mateo, CA: Morgan Kaufmann; 1999. p. 657–664. Tsutsui S, Yamamura M, Higuchi T. Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the genetic and evolutionary computation conference (GECCO), Orlando, FL, USA, July 1999. San Mateo, CA: Morgan Kaufmann; 1999. p. 657–664.
94.
go back to reference Ursem RK. Diversity-guided evolutionary algorithms. In: Proceedings of the 7th conference on parallel problem solving from nature (PPSN VII), Granada, Spain, Sept 2002. p. 462–471. Ursem RK. Diversity-guided evolutionary algorithms. In: Proceedings of the 7th conference on parallel problem solving from nature (PPSN VII), Granada, Spain, Sept 2002. p. 462–471.
95.
go back to reference Voigt HM, Muhlenbein H, Cvetkovic D. Fuzzy recombination for the breeder genetic algorithm. In: Eshelman L, editor. Proceedings of the 6th international conference on genetic algorithms, Pittsburgh, PA, USA, July 1995. San Mateo, CA: Morgan Kaufmann; 1995. p. 104–111. Voigt HM, Muhlenbein H, Cvetkovic D. Fuzzy recombination for the breeder genetic algorithm. In: Eshelman L, editor. Proceedings of the 6th international conference on genetic algorithms, Pittsburgh, PA, USA, July 1995. San Mateo, CA: Morgan Kaufmann; 1995. p. 104–111.
96.
go back to reference Whitley D. The GENITOR algorithm and selective pressure. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 116–121. Whitley D. The GENITOR algorithm and selective pressure. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 116–121.
97.
go back to reference Whitley D, Starkweather T, Fuquay D. Scheduling problems and traveling salesmen: the genetic edge recombination operator. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 133–140. Whitley D, Starkweather T, Fuquay D. Scheduling problems and traveling salesmen: the genetic edge recombination operator. In: Proceedings of the 3rd international conference on genetic algorithms, Fairfax, VA, USA, June 1989. San Mateo, CA: Morgan Kaufmann; 1989. p. 133–140.
98.
go back to reference Wright AH. Genetic algorithms for real parameter optimization. In: Rawlins G, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. p. 205–218. Wright AH. Genetic algorithms for real parameter optimization. In: Rawlins G, editor. Foundations of genetic algorithms. San Mateo, CA: Morgan Kaufmann; 1991. p. 205–218.
99.
go back to reference Yao X, Liu Y, Liang KH, Lin G. Fast evolutionary algorithms. In: Ghosh S, Tsutsui S, editors. Advances in evolutionary computing: theory and applications. Berlin, Springer; 2003. p. 45–9. Yao X, Liu Y, Liang KH, Lin G. Fast evolutionary algorithms. In: Ghosh S, Tsutsui S, editors. Advances in evolutionary computing: theory and applications. Berlin, Springer; 2003. p. 45–9.
100.
go back to reference Yip PPC, Pao YH. Combinatorial optimization with use of guided evolutionary simulated annealing. IEEE Trans Neural Networks. 1995;6(2):290–5.CrossRef Yip PPC, Pao YH. Combinatorial optimization with use of guided evolutionary simulated annealing. IEEE Trans Neural Networks. 1995;6(2):290–5.CrossRef
101.
go back to reference Yukiko Y, Nobue A. A diploid genetic algorithm for preserving population diversity—pseudo-meiosis GA. In: Parallel problem solving from nature (PPSN III), Vol. 866 of the series Lecture Notes in Computer Science. Berlin: Springer; 1994. p. 36–45. Yukiko Y, Nobue A. A diploid genetic algorithm for preserving population diversity—pseudo-meiosis GA. In: Parallel problem solving from nature (PPSN III), Vol. 866 of the series Lecture Notes in Computer Science. Berlin: Springer; 1994. p. 36–45.
Metadata
Title
Genetic Algorithms
Authors
Ke-Lin Du
M. N. S. Swamy
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-41192-7_3

Premium Partner