Skip to main content

Evolutionary Algorithms

  • Chapter
Intelligent Systems

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 17))

Introduction

In nature, evolution is mostly determined by natural selection of different individuals competing for resources in the environment. Those individuals that are better are more likely to survive and propagate their genetic material. The encoding for genetic information (genome) is done in a way that admits asexual reproduction, which results in offspring that are genetically identical to the parent. Sexual reproduction allows some exchange and re-ordering of chromosomes, producing offspring that contain a combination of information from each parent. This is the recombination operation, which is often referred to as crossover because of the way strands of chromosomes cross over during the exchange. The diversity in the population is achieved by mutation operation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bäck, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. IOP Publishing and Oxford University Press, New York (1997)

    Book  MATH  Google Scholar 

  2. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  3. Eiben, A.E., Aarts, E.H.L., van Hee, K.M.: Global convergence of genetic algorithms: a markov chain analysis. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 4–12. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  4. Bäck, T.: Evolutionary algorithms in theory and practice. Oxford University Press, New York (1996)

    MATH  Google Scholar 

  5. Fogel, D.B.: Evolutionary Computation. IEEE Press, Los Alamitos (1995)

    Google Scholar 

  6. Bäck, T.: Generalized convergence models for tournament and (μ, λ) selection. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 2–8 (1995)

    Google Scholar 

  7. Blickle, T., Thiele, L.: A comparison of selection schemes used in genetic algorithms. Evolutionary Computation 4(4), 361–394 (1996)

    Article  Google Scholar 

  8. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms 1, 69–93 (1991)

    MathSciNet  Google Scholar 

  9. Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)

    Google Scholar 

  10. Goldberg, D.E.: Generic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)

    Google Scholar 

  11. Baker, J.E.: Reducing Bias and Inefficiency in the Selection Algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 14–21. Lawrence Erlbaum Associates, Hillsdale (1987)

    Google Scholar 

  12. Evolutionary algorithms tutorial, http://www.geatbx.com/

  13. Bäck, T., Hoffmeister, F.: Extended Selection Mechanisms in Genetic Algorithms. In: Bäck, T., Hoffmeister, F. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 92–99 (1991)

    Google Scholar 

  14. Whitley, D.: The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. In: Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 116–121 (1989)

    Google Scholar 

  15. Pohlheim, H.: Ein genetischer Algorithmus mit Mehrfachpopulationen zur Numerischen Optimierung. at-Automatisierungstechnik 3, 127–135 (1995)

    Google Scholar 

  16. Syswerda, G.: Uniform crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 2–9 (1989)

    Google Scholar 

  17. Spears, W.M., De Jong, K.A.: On the Virtues of Parameterised Uniform Crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, California, USA, pp. 230–236 (1991)

    Google Scholar 

  18. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)

    MATH  Google Scholar 

  19. Goldberg, D.E., Lingle, R.: Alleles, loci and the traveling salesman problem. In: Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 154–159. Lawrence Erlbaum, Hillsdale (1985)

    Google Scholar 

  20. Whitley, D.: Permutations, In Evolutionary Computation 1: Basic Algorithms and Operators. In: Bäck, T., Fogel, D.B. (eds.), pp. 274–284. Institute of Physics Publishing, Bristol (2000)

    Google Scholar 

  21. Davis, L. (ed.): Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  22. Olivier, L.M., Smith, D.J., Holland, J.: A study of permutation crossover operators on the traveling salesman problem. In: Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, pp. 224–230. Lawrence Erlbaum, Hillsdale (1987)

    Google Scholar 

  23. Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive Models for the Breeder Genetic Algorithm: I. Continuous Parameter Optimization. Evolutionary Computation 1(1), 25–49 (1993)

    MATH  Google Scholar 

  24. Yao, X., Liu, Y.: Fast evolutionary programming, In. In: Proceedings of the Fifth Annual Conference on Evolutionary Programming, pp. 451–460. The MIT Press, Cambridge (1996)

    Google Scholar 

  25. Mühlenbein, H., Pass, G.: From recombination of genes to the estimation of distributions I. Binary parameters. In: Proceedings of the 4th Conference on Parallel Problems Solving from Nature, pp. 188–197 (1996)

    Google Scholar 

  26. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Fromman-Holzboog, Stuttgart (1973)

    Google Scholar 

  27. Schwefel, H.P.: Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie. Birkhaeuser, Basel (1977)

    Google Scholar 

  28. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial intelligence through simulated evolution. Wiley, Chichester (1966)

    MATH  Google Scholar 

  29. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means o f Natural Selection. MIT Press, Cambridge (1992)

    Google Scholar 

Download references

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Grosan, C., Abraham, A. (2011). Evolutionary Algorithms. In: Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21004-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21004-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21003-7

  • Online ISBN: 978-3-642-21004-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics