Abstract
This paper investigates the behaviour of the Evolvable Agent model (EvAg) in static and dynamic environments. The EvAg is a spatially structured Genetic Algorithm (GA) designed to work on Peer-to-Peer (P2P) systems in which the population structure is a small-world graph built by newscast, a P2P protocol. Additionally to the profits in computing performance, EvAg maintains genetic diversity at the small-world relationships between individuals in a sort of social network. Experiments were conducted in order to assess how EvAg scales on deceptive and non-deceptive trap functions. In addition, the proposal was tested on dynamic environments. The results show that the EvAg scales and adapts better to dynamic environments than a standard GA and an improved version of the well-known Random Immigrants Genetic Algorithm. © 2008 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Laredo, J. L. J., Castillo, P. A., Mora, A. M., Merelo, J. J., Rosa, A., & Fernandes, C. (2008). Evolvable agents in static and dynamic optimization problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5199 LNCS, pp. 488–497). https://doi.org/10.1007/978-3-540-87700-4_49
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