Evolvable Agents in Static and Dynamic Optimization Problems

Authors:
Juan L. Laredo;Pedro A. Castillo;Antonio M. Mora;Juan J. Merelo;Agostinho Rosa;Carlos Fernandes
Affiliations:
Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;Department of Architecture and Computer Technology, University of Granada, Spain;LASEEB-ISR/IST, University of Lisbon, Portugal;Department of Architecture and Computer Technology, University of Granada, Spain and LASEEB-ISR/IST, University of Lisbon, Portugal
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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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.