Skip to main content
SpringerLink
Log in

Evolutionary computing in multi-agent environments: Operators

  • Conference paper
  • First Online:
Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

Included in the following conference series:

Abstract

This paper examines a key aspect of applying evolutionary computing techniques to multi-agent systems: a comparison in the performance of the genetic operators of mutation and recombination. Using the tuneable NKC model of multi-agent evolution it is shown that the benefits of simple recombination and mutation vary depending on the type of system, with bit mutation capable of doing as well as recombination in systems with significant inter-agent epistasis. The effects of fitness sharing between the interacting individuals are then examined and it is shown that mutation can do as well as, or better than, recombination even under low inter-agent epistasis; fitness sharing is shown to alter the characteristics of the coevolving system.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Axlerod R (1987), “The Evolution of Strategies in the Iterated Prisoner's Dilemma,” in L Davis (ed.) Genetic Algorithms and Simulated Annealing, Pittman, pp32–42.

    Google Scholar 

  • Baeck T (1991), “Self Adaptation in Genetic Algorithms,” in F J Varela & P Borgine (eds.) Toward a Practice of Autonomous Systems — Proceedings of the First European Conference on Artificial Life, MIT Press, pp263–271.

    Google Scholar 

  • Bull L (1997), “Evolutionary Computing in Multi-Agent Environments: Partners,” in T Baeck (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufmann, pp370–377.

    Google Scholar 

  • Bull L, Fogarty T C & Snaith M (1995), “Evolution in Multi-Agent Systems: Evolving Communicating Classifier Systems for Gait in a Quadrupedal Robot,” in L J Eshelman (ed.) Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, pp382–388.

    Google Scholar 

  • Davis L (1991), “Bit-climbing, Representational Bias, and Test-suite Design,” in R K Belew & L B Booker (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, pp18–23.

    Google Scholar 

  • DeJong K A (1975), “An Analysis of the Behaviour of a Class of Genetic Adaptive Systems,” PhD Dissertation, Universtiy of Michigan.

    Google Scholar 

  • Eshelman L J & Schaffer J D (1993), “Crossover's Niche,” in S Forrest (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, pp9–14.

    Google Scholar 

  • Fogel D B & Atmar J W (1990), “Comparing Genetic Operators with Gaussian Mutations in Simulated Evolutionary Processes using Linear Systems,” Biological Cybernetics 63:111–114.

    Google Scholar 

  • Forrest S & Mitchell M (1992), “Relative Building-Block Fitness and the Building Block Hypothesis,” in D Whitley (ed.) Foundations of Genetic Algorithms 2, Morgan Kaufmann, pp109–126.

    Google Scholar 

  • Holland J H (ed.)(1975), Adaptation in Natural and Artificial Systems, University of Michigan Press.

    Google Scholar 

  • Kauffman S A (ed.)(1993), The Origins of Order: Self-organisation and Selection in Evolution, Oxford University Press.

    Google Scholar 

  • Law R (1985), “Evolution in a Mutualistic Environment,” in P Buchner (ed.) The Biology of Mutualism, Croom-Helm, pp145–170.

    Google Scholar 

  • Michod R E & Levin B R (eds.)(1988) The Evolution of Sex: an Examination of Current Ideas, Sinauer Associates.

    Google Scholar 

  • Mikami S & Kakazu Y (1994), “Cooperation of Multiple Agents Through Competitive Payoff,” in Machine Learning: ECML 95, Springer-Verlag, pp319–322.

    Google Scholar 

  • Potter M & DeJong K (1994), “A Cooperative Coevolutionary Approach to Function Optimisation,” in Y Davidor, H-P Schwefel & R Manner (eds.) Parallel Problem Solving from Nature — PPSN III, Springer-Verlag, pp249–259.

    Google Scholar 

  • Schaffer J D & Eshelman L J (1991), “On Crossover as an Evolutionarily Viable Strategy,” in R K Belew & L B Booker (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, pp61–68.

    Google Scholar 

  • Seredynski F (1994), “Loosely Coupled Distributed Genetic Algorithms,” in Y Davidor, H-P Schwefel & R Manner (eds.) Parallel Problem Solving From Nature — PPSN III, Springer-Verlag, pp514–523.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Computer Systems Centre, University of the West of England, BS16 1QY, Bristol, UK

    Larry Bull

Authors
  1. Larry Bull
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bull, L. (1998). Evolutionary computing in multi-agent environments: Operators. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040758

Download citation

  • DOI: https://doi.org/10.1007/BFb0040758

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation