The Effect of Mutation on the Accumulation of Information in a Genetic Algorithm

  • John Milton
  • Paul Kennedy
  • Heather Mitchell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)


We use an information theory approach to investigate the role of mutation on Genetic Algorithms (GA). The concept of solution alleles representing information in the GA and the associated concept of information density, being the average frequency of solution alleles in the population, are introduced. Using these concepts, we show that mutation applied indiscriminately across the population has, on average, a detrimental effect on the accumulation of solution alleles within the population and hence the construction of the solution. Mutation is shown to reliably promote the accumulation of solution alleles only when it is targeted at individuals with a lower information density than the mutation source. When individuals with a lower information density than the mutation source are targeted for mutation, very high rates of mutation can be used. This significantly increases the diversity of alleles present in the population, while also increasing the average occurrence of solution alleles.


Evolutionary computing genetic algorithm mutation information theory 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fraser, A.S.: Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Science 10, 484–491 (1957)Google Scholar
  2. 2.
    Box, G.E.P.: Evolutionary operation: a method of increasing industrial productivity. Applied Statistics 6, 81–101 (1957)CrossRefGoogle Scholar
  3. 3.
    Bremermann, H.J., Rogson, M., Salaff, S.: Global properties of evolution processes. In: Pattee, H.H., Edlsack, E.A., Fein, L., Callahan, A.B. (eds.) Natural Automata and Useful Simulation, pp. 3–41. Spartan Books, Washington DC (1966)Google Scholar
  4. 4.
    Holland, J.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor. MIT Press, Cambridge (1975)Google Scholar
  5. 5.
    Rechenberg, I.: Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der iologischen Evolution. Frommann-Holzboog, Stuttgart, DE (1973)Google Scholar
  6. 6.
    Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison Wesley, Reading (1989)zbMATHGoogle Scholar
  7. 7.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1999)Google Scholar
  8. 8.
    Goldberg, D.: The Design of Innovation: lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  9. 9.
    Koza, J.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  10. 10.
    Schwefel, H.: Numerical Optimization of Computer Models. John Wiley & Sons, New York (1981)zbMATHGoogle Scholar
  11. 11.
    Beyer, H.: An alternative explanation for the manner in which genetic algorithms operate. Elsevier Science, Biosystems 41(1), 1–15 (1997)Google Scholar
  12. 12.
    Stephens, C., Waelbroeck, H.: Schemata evolution and building blocks. Evolutionary Computation 7(2), 109–124 (1999)CrossRefGoogle Scholar
  13. 13.
    Whitley, D.: An overview of evolutionary algorithms: practical issues and common pitfalls. Information and Software Technology 43, 817–831 (2001)CrossRefGoogle Scholar
  14. 14.
    Van der Lubbe, J.: Information Theory. Cambridge University Press, Cambridge (1997)Google Scholar
  15. 15.
    Bala, J., Huang, J., Vafaie, H., DeJong, K., Wechsler, H.: Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification. In: IJCAI conference, Montreal, pp. 19–25 (1995)Google Scholar
  16. 16.
    Araujo, D., Lopes, H., Freitas, A.: A Parallel Genetic Algorithm for Rule Discovery in Large Databases. In: Proceedings IEEE Systems, Man and Cybernetics Conf., Tokyo, vol. III, pp. 940–945 (1999)Google Scholar
  17. 17.
    Shipman, R., Shackleton, M., Ebner, M., Watson, R.: Neutral Search Spaces for Artificial Evolution: a lesson from life’: Artificial Life. In: Proceedings of Seventh International Conference on Artificial Life. MIT Press, Cambridge (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • John Milton
    • 1
  • Paul Kennedy
    • 1
  • Heather Mitchell
    • 2
  1. 1.Faculty of Information TechnologyUniversity of Technology Sydney, Australia UTSBroadway, SydneyAustralia
  2. 2.School of Economics, Finance and MarketingRoyal Melbourne Institute of TechnologyMelbourneAustralia

Personalised recommendations