Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction to Evolutionary Single-Objective Optimisation Read chapter Read first chapter

2019 | OriginalPaper | Chapter

4. Genetic Algorithm

Author : Seyedali Mirjalili

Published in: Evolutionary Algorithms and Neural Networks

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Genetic Algorithm (GA) is one of the first population-based stochastic algorithm proposed in the history. Similar to other EAs, the main operators of GA are selection, crossover, and mutation. This chapter briefly presents this algorithm and applies it to several case studies to observe its performance.

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!

inform now

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!

inform now

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!

inform now
previous chapter Ant Colony Optimisation
next chapter Biogeography-Based Optimisation
Literature
1.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.CrossRef
2.
Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1), 66–73.CrossRef
3.
Genlin, J. (2004). Survey on genetic algorithm. Computer Applications and Software, 2, 69–73.
4.
Cant-Paz, E. (1998). A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2), 141–171.
5.
Goldberg, D. E., & Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. In Foundations of genetic algorithms (Vol. 1, pp. 69–93). Elsevier.
6.
Goldberg, D. E. (1990). A note on Boltzmann tournament selection for genetic algorithms and population-oriented simulated annealing. Complex Systems, 4(4), 445–460.MATH
7.
Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9(3), 193–212.MathSciNet
8.
Kumar, R. (2012). Blending roulette wheel selection & rank selection in genetic algorithms. International Journal of Machine Learning and Computing, 2(4), 365.CrossRef
9.
Syswerda, G. (1991). A study of reproduction in generational and steady-state genetic algorithms. In Foundations of genetic algorithms (Vol. 1, pp. 94–101). Elsevier.
10.
Blickle, T., & Thiele, L. (1996). A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation, 4(4), 361–394.CrossRef
11.
Collins, R. J., & Jefferson, D. R. (1991). Selection in massively parallel genetic algorithms (pp. 249–256). University of California (Los Angeles), Computer Science Department.
12.
Ishibuchi, H., & Yamamoto, T. (2004). Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems, 141(1), 59–88.MathSciNetCrossRef
13.
Hutter, M. (2002). Fitness uniform selection to preserve genetic diversity. In Proceedings of the 2002 Congress on Evolutionary Computation, CEC’02 (Vol. 1, pp. 783–788). IEEE.
14.
Grefenstette, J. J. (1989). How genetic algorithms work: A critical look at implicit parallelism. In Proceedings of the 3rd International Joint Conference on Genetic Algorithms (ICGA89).
15.
Syswerda, G. (1989). Uniform crossover in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms (pp. 2–9). Morgan Kaufmann Publishers.
16.
Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms: A survey. Computer, 27(6), 17–26.CrossRef
17.
Semenkin, E., & Semenkina, M. (2012). Self-configuring genetic algorithm with modified uniform crossover operator. In International Conference in Swarm Intelligence (pp. 414–421). Heidelberg: Springer.
18.
Hu, X. B., & Di Paolo, E. (2007). An efficient genetic algorithm with uniform crossover for the multi-objective airport gate assignment problem. In IEEE Congress on Evolutionary Computation, 2007 (CEC 2007) (pp. 55–62). IEEE.
19.
Tsutsui, S., Yamamura, M., & Higuchi, T. (1999). Multi-parent recombination with simplex crossover in real coded genetic algorithms. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1 (pp. 657–664). Morgan Kaufmann Publishers Inc.
20.
Bck, T., Fogel, D. B., & Michalewicz, Z. (Eds.). (2000). Evolutionary computation 1: Basic algorithms and operators (Vol. 1). CRC press.
21.
Oliver, I. M., Smith, D., & Holland, J. R. (1987). Study of permutation crossover operators on the travelling salesman problem. In Proceedings of the Second International Conference on Genetic Algorithms and their Applications, July 28–31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates.
22.
Davis, L. (1985). Applying adaptive algorithms to epistatic domains. In IJCAI (Vol. 85, pp. 162–164).
23.
Whitley, D., Timothy, S., & Daniel, S. Schedule optimization using genetic algorithms. In D. Lawrence (Ed.) 351–357.
24.
Grefenstette, J., Gopal, R., Rosmaita, B., & Van Gucht, D. (1985). Genetic algorithms for the traveling salesman problem. In Proceedings of the first International Conference on Genetic Algorithms and their Applications (pp. 160–168).
25.
Louis, S. J., & Rawlins, G. J. (1991). Designer genetic algorithms: Genetic algorithms in structure design. In ICGA (pp. 53–60).
26.
Eshelman, L. J., Caruana, R. A., & Schaffer, J. D. (1989). Biases in the crossover landscape. In Proceedings of the Third International Conference on Genetic Algorithms (pp. 10–19). Morgan Kaufmann Publishers Inc.
27.
Deep, K., & Thakur, M. (2007). A new mutation operator for real coded genetic algorithms. Applied Mathematics and Computation, 193(1), 211–230.MathSciNetCrossRef
28.
Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 24(4), 656–667.CrossRef
29.
Neubauer, A. (1997). A theoretical analysis of the non-uniform mutation operator for the modified genetic algorithm. In IEEE International Conference on Evolutionary Computation (pp. 93–96). IEEE.
30.
Hinterding, R. (1995). Gaussian mutation and self-adaption for numeric genetic algorithms. In IEEE International Conference on Evolutionary Computation (Vol. 1, p. 384). IEEE.
31.
Tsutsui, S., & Fujimoto, Y. (1993). Forking genetic algorithm with blocking and shrinking modes (fGA). In ICGA (pp. 206–215).
32.
Oosthuizen, G. D. (1987). Supergran: A connectionist approach to learning, integrating genetic algorithms and graph induction. In Proceedings of the second International Conference on Genetic Algorithms and their Applications, July 28–31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, NJ: L. Erlhaum Associates.
33.
Mauldin, M. L. (1984). Maintaining diversity in genetic search. In AAAI (pp. 247–250).
34.
Ankenbrandt, C. A. (1991). An extension to the theory of convergence and a proof of the time complexity of genetic algorithms. In Foundations of genetic algorithms (Vol. 1, pp. 53–68). Elsevier.
35.
Ahn, C. W., & Ramakrishna, R. S. (2003). Elitism-based compact genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(4), 367–385.CrossRef
36.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.CrossRef
Metadata
Title
Genetic Algorithm
Author
Seyedali Mirjalili
Copyright Year
2019
Book
Evolutionary Algorithms and Neural Networks

Print ISBN: 978-3-319-93024-4

Electronic ISBN: 978-3-319-93025-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-93025-1_4

Premium Partner

    Image Credits