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A Model of Co-evolution in Multi-agent System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2691))

Abstract

Co-evolutionary techniques are aimed at overcoming limited adaptive capacity of evolutionary algorithms resulting from the loss of useful diversity of population. In this paper the idea of co-evolutionary multi-agent system (CoEMAS) is introduced. In such a system two or more species of agents co-evolve in order to solve given problem. Also, the formal model of CoEMAS and the results from runs of CoEMAS applied to multi-modal function optimization are presented.

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References

  1. P. J. Angeline and J. B. Pollack. Competitive environments evolve better solutions for complex tasks. In Proc. of the 5th Int. Conf. on Genetic Algorithms (GA-93), 1993.

    Google Scholar 

  2. E. Cetnarowicz, E. Nawarecki, and K. Cetnarowicz. Agent oriented technology of decentralized systems based on the m-agent architecture. In Proceedings of the Management and Control of Production and Logistics Conference-MCPL’97, Sao Paulo, Brazil, 1997. IFAC, PERGAMON.

    Google Scholar 

  3. K. Cetnarowicz, M. Kisiel-Dorohinicki, and E. Nawarecki. The application of evolution process in multi-agent world to the prediction system. In Proc. of the 2nd Int. Conf. on Multi-Agent Systems—ICMAS’96, Osaka, Japan, 1996. AAAI Press.

    Google Scholar 

  4. P. J. Darwen and X. Yao. On evolving robust strategies for iterated prisoner’s dilemma. Lecture Notes in Computer Science, 956, 1995.

    Google Scholar 

  5. D.E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, editor, Proc. of the 2nd Int. Conf. on Genetic Algorithms, Hillsdale, NJ, 1987. Lawrence Erlbaum Associates.

    Google Scholar 

  6. D.E. Goldberg and L. Wang. Adaptive niching via coevolutionary sharing. Technical Report IlliGAL 97007, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 1997.

    Google Scholar 

  7. T. Haynes and S. Sen. Evolving behavioral strategies in predators and prey. In S. Sen, editor, IJCAI-95 Workshop on Adaptation and Learning in Multiagent Systems, Montreal, Quebec, Canada, 1995. Morgan Kaufmann.

    Google Scholar 

  8. T. Haynes and S. Sen. The evolution of multiagent coordination strategies. Adaptive Behavior, 1997.

    Google Scholar 

  9. M. Kisiel-Dorohinicki. Agent-oriented model of simulated evolution. In W. I. Grosky and F. Plasil, editors, SofSem 2002: Theory and Practice of Informatics, volume 2296 of Lecture Notes in Computer Science. Springer-Verlag, 2002.

    Google Scholar 

  10. S.W. Mahfoud. Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 1995.

    Google Scholar 

  11. J. Morrison and F. Oppacher. A general model of co-evolution for genetic algorithms. In Int. Conf. on Artificial Neural Networks and Genetic Algorithms ICANNGA 99, 1999.

    Google Scholar 

  12. J. Paredis. Coevolutionary algorithms. In T. Bäck, D. Fogel, and Z. Michalewicz, editors, Handbook of Evolutionary Computation, 1st supplement. IOP Publishing and Oxford University Press, 1998.

    Google Scholar 

  13. M.A. Potter and K. De Jong. A cooperative coevolutionary approach to function optimization. In Y. Davidor, H.-P. Schwefel, and R. Männer, editors, Parallel Problem Solving from Nature—PPSN III, Berlin, 1994. Springer.

    Google Scholar 

  14. M. A. Potter and K. A. De Jong. Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation, 8(1), 2000.

    Google Scholar 

  15. C.H. Yong and R. Miikkulainen. Cooperative coevolution of multi-agent systems. Technical Report AI01-287, Department of Computer Sciences, University of Texas at Austin, 2001.

    Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Dreżewski, R. (2003). A Model of Co-evolution in Multi-agent System. In: Mařík, V., Pěchouček, M., Müller, J. (eds) Multi-Agent Systems and Applications III. CEEMAS 2003. Lecture Notes in Computer Science(), vol 2691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45023-8_30

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  • DOI: https://doi.org/10.1007/3-540-45023-8_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40450-7

  • Online ISBN: 978-3-540-45023-8

  • eBook Packages: Springer Book Archive

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