Abstract
Co-evolutionary techniques for evolutionary algorithms are aimed at overcoming their limited adaptive capabilities and allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. In this paper the idea of co-evolutionary multi-agent system with host-parasite mechanism for multi-objective optimization is introduced. In presented system the Pareto frontier is located by the population of agents as a result of co-evolutionary interactions between species. Also, results from runs of presented system against test functions are presented.
Chapter PDF
Similar content being viewed by others
Keywords
- Evolutionary Algorithm
- Multiobjective Optimization
- Pareto Frontier
- Adaptive Capability
- Strength Pareto Evolutionary Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Cetnarowicz, K., Kisiel-Dorohinicki, M., Nawarecki, E.: The application of evolution process in multi-agent world to the prediction system. In: Tokoro, M. (ed.) Proceedings of the 2nd International Conference on Multi-Agent Systems (ICMAS 1996). AAAI Press, Menlo Park (1996)
Deb, K.: Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation 7(3), 205–230 (1999)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Dreżewski, R.: A model of co-evolution in multi-agent system. In: Mařík, V., Müller, J.P., Pěchouček, M. (eds.) CEEMAS 2003. LNCS, vol. 2691, pp. 314–323. Springer, Heidelberg (2003)
Horn, J., Nafploitis, N., Goldberg, D.: A niched pareto genetic algorithm for multi-objective optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82–87 (1994)
Laumanns, M., Rudolph, G., Schwefel, H.-P.: A spatial predator-prey approach to multi-objective optimization: A preliminary study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998, vol. 1498. Springer, Heidelberg (1998)
Li, X.: A real-coded predator-prey genetic algorithm for multiobjective optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 207–221. Springer, Heidelberg (2003)
Paredis, J.: Coevolutionary algorithms. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, 1st supplement. IOP Publishing and Oxford University Press (1998)
Schaffer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. PhD thesis, Vanderbilt University (1984)
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 93–100 (1985)
Siwik, L., Kisiel-Dorohinicki, M.: Balancing of production lines: evolutionary, agent-based approach. In: Proceedings of Conference on Management and Control of Production and Logistics, pp. 319–324 (2004)
Socha, K., Kisiel-Dorohinicki, M.: Agent-based evolutionary multiobjective optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 109–114 (2002)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dreżewski, R., Siwik, L. (2006). Multi-objective Optimization Using Co-evolutionary Multi-agent System with Host-Parasite Mechanism. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758532_113
Download citation
DOI: https://doi.org/10.1007/11758532_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34383-7
Online ISBN: 978-3-540-34384-4
eBook Packages: Computer ScienceComputer Science (R0)