Evolvable Agents in Static and Dynamic Optimization Problems
نویسندگان
چکیده
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-toPeer (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 smallworld 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.
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تاریخ انتشار 2008