Evolutionary Multi-Agent Systems in Non-Stationary Environments

Authors

  • Marek Kisiel-Dorohinicki AGH University of Science and Technology

DOI:

https://doi.org/10.7494/csci.2013.14.4.563

Abstract

In the article the performance of an evolutionary multi-agent system in  dynamic optimization is evaluated in comparison to classical evolutionary  algorithms.  The starting point is a general introduction describing the  background, structure and behaviour of EMAS against classical  evolutionary techniques.  Then the properties of energy-based selection  are investigated to show how it may influence the diversity of the  population in EMAS.  The considerations are illustrated by experimental  results based on the dynamic version of the well-known, high-dimensional Rastrigin function  benchmark.

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References

T. Bäck, D. Fogel, and Z. Michalewicz, editors. Handbook of Evolutionary Computa-

tion. IOP Publishing and Oxford University Press, 1997.

A. Byrski. Markov chain analysis of agent-based evolutionary computing in dynamic

optimization. Computing and Informatics—Submitted for review, X(X), 2012/2013.

A. Byrski and M. Kisiel-Dorohinicki. Agent-based evolutionary and immunological

optimization. In Computational Science - ICCS 2007, 7th International Conference,

Beijing, China, May 27 - 30, 2007, Proceedings. Springer, 2007.

E. Cantú-Paz. A summary of research on parallel genetic algorithms. IlliGAL Report

No. 95007. University of Illinois, 1995.

K. Cetnarowicz, M. Kisiel-Dorohinicki, and E. Nawarecki. The application of evolution

process in multi-agent world (MAW) to the prediction system. In M. Tokoro, editor,

Proc. of the 2nd Int. Conf. on Multi-Agent Systems (ICMAS’96). AAAI Press, 1996.

Krzysztof Cetnarowicz. Evolution in multi-agent world = genetic algorithms + ag-

gregation + escape. In 7th European Workshop on Modelling Autonomous Agents in

a Multi-Agent World (MAAMAW’96). Vrije Universiteit Brussel, Artificial Intelligence

Laboratory, 1996.

R. Drezewski. Co-evolutionary multi-agent system with speciation and resource sharing

mechanisms. Computing and Informatics, 25(4), 2006.

David B. Fogel. Evolutionary Computation: The Fossil Record. Selected Readings on

the History of Evolutionary Computation. IEEE Press, 1998.

D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning.

Massachusetts: Addison-Wesley, 1989.

D.E. Goldberg and R.E. Smith. Nonstationary function optimization using genetic algo-

rithms with dominance and diploidy. In Proc. of the Second International Conference

on Genetic Algorithms, pages 59 ˝U–68, 1987.

R. Horst and P. Pardalos. Handbook of Global Optimization. Kluwer, 1995.

Y. Jin and J. Branke. Evolutionary optimization in uncertain environment—a survey.

IEEE Transactions on Evolutionary Computation, 9:303–317, 2005.

P. Jojczyk and R. Schaefer. Global impact balancing in the hierarchic genetic search.

Computing and Informatics, 28(2), 2008.

M. Kisiel-Dorohinicki. Agent-Oriented Model of Simulated Evolution. In William I.

Grosky and Frantisek Plasil, editors, SofSem 2002: Theory and Practice of Informatics,

volume 2540 of LNCS. Springer-Verlag, 2002.

Mark Lutz. Programming Python. O’Reilly Media, 2011.

S. W. Mahfoud. Niching methods for genetic algorithms. PhD thesis, University of

Illinois at Urbana-Champaign, Urbana, IL, USA, 1995.

Ronald W. Morrison and Kenneth A. De Jong. Measurement of population diversity. In

P. Collet et al., editor, Proc. of EA 2001, LNCS 2310, pages 31–41. Springer, 2002.

J. Paredis. Coevolutionary computation. Artificial Life, 2(4):355–375, 1995.

K. Pietak, A. Wo´s, A. Byrski, and M. Kisiel-Dorohinicki. Functional integrity of multiagent computational system supported by component-based implementation. In Pro-

ceedings of the 4th International Conference on Industrial Applications of Holonic and

Multi-agent Systems, 2009.

M. A. Potter and K. A. De Jong. Cooperative coevolution: An architecture for evolving

coadapted subcomponents. Evolutionary Computation, 8(1):1–29, 2000.

A. Simoes and E. Costa. Improving prediction in evolutionary algorithms for dynamic

environments. In Proc. of the 2009 Genetic and Evolutionary Computation Conference,

pages 875 ˝U–888, 2009.

Krzysztof Trojanowski and Zbigniew Michalewicz. Searching for optima in non-

stationary environments. In Proc. of the Congress on Evolutionary Computation. Wash-

ington, USA., volume 3, pages 1843–1850. IEEE Press, 1999.

Karsten Weicker. Evolutionary Algorithms and Dynamic Optimization Problems. PhD

thesis, University of Stuttgart, 2003.

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Published

2013-11-22

How to Cite

Kisiel-Dorohinicki, M. (2013). Evolutionary Multi-Agent Systems in Non-Stationary Environments. Computer Science, 14(4), 563. https://doi.org/10.7494/csci.2013.14.4.563

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