Abstract
It turns out that hybridizing agent-based paradigm with evolutionary computation brings a new quality to the field of meta-heuristics, enhancing individuals with possibilities of perception, interaction with other individuals (agents), adaptation of parameters, etc. In the paper such technique—an evolutionary multi-agent system (EMAS)—is compared with a classical evolutionary algorithm (Michalewicz model) implemented with allopatric speciation (island model). Both algorithms are applied to the problem of continuous optimisation in selected benchmark problems. The results are very promising, as agent-based computing turns out to be more effective than classical one, especially in difficult benchmark problems, such as high-dimensional Rastrigin function.
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References
Kisiel-Dorohinicki, M., Dobrowolski, G., Nawarecki, E.: Agent populations as computational intelligence. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. Advances in Soft Computing, Physica-Verlag (2003)
Sarker, R., Ray, T.: Agent-Based Evolutionary Search. Springer (2010)
Chen, S.H., Kambayashi, Y., Sato, H.: Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies. IGI Global (2011)
Schaefer, R., Kołodziej, J.: Genetic search reinforced by the population hierarchy. Foundations of Genetic Algorithms 7 (2003)
Kisiel-Dorohinicki, M.: Agent-Oriented Model of Simulated Evolution. In: Grosky, W.I., Plášil, F. (eds.) SOFSEM 2002. LNCS, vol. 2540, pp. 253–261. Springer, Heidelberg (2002)
Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1128–1141 (2004)
Byrski, A., Dreżewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. The Knowledge Engineering Review (2012) (accepted for publication)
Back, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: Comments on the history and current state. IEEE Trans. on Evolutionary Computation 1(1) (1997)
Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois (1995)
Dreżewski, R., Cetnarowicz, K.: Sexual Selection Mechanism for Agent-Based Evolutionary Computation. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part II. LNCS, vol. 4488, pp. 920–927. Springer, Heidelberg (2007)
Wolfram, S.: A New Kind of Science. Wolfram Media (2002)
Byrski, A., Kisiel-Dorohinicki, M.: Agent-Based Model and Computing Environment Facilitating the Development of Distributed Computational Intelligence Systems. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part II. LNCS, vol. 5545, pp. 865–874. Springer, Heidelberg (2009)
Lutz, M.: Programming Python. O’Reilly Media (2011)
Michalewicz, Z.: Genetic Algorithms Plus Data Structures Equals Evolution Programs. Springer-Verlag New York, Inc., Secaucus (1994)
Morrison, R.W., De Jong, K.A.: Measurement of Population Diversity. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 31–41. Springer, Heidelberg (2002)
Digalakis, J., Margaritis, K.: An experimental study of benchmarking functions for evolutionary algorithms. Int. J. of Computer Mathemathics 79(4), 403–416 (2002)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
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Pisarski, S., Rugała, A., Byrski, A., Kisiel-Dorohinicki, M. (2013). Evolutionary Multi-Agent System in Hard Benchmark Continuous Optimisation. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_14
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DOI: https://doi.org/10.1007/978-3-642-37192-9_14
Publisher Name: Springer, Berlin, Heidelberg
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