Evolvable Agents in Static and Dynamic Optimization Problems

  • Juan L. J. Laredo
  • Pedro A. Castillo
  • Antonio M. Mora
  • Juan J. Merelo
  • Agostinho Rosa
  • Carlos Fernandes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


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.


Evolvable Agent Bias Error Cache Size Dynamic Optimization Problem Standard Genetic 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Juan L. J. Laredo
    • 1
  • Pedro A. Castillo
    • 1
  • Antonio M. Mora
    • 1
  • Juan J. Merelo
    • 1
  • Agostinho Rosa
    • 2
  • Carlos Fernandes
    • 1
    • 2
  1. 1.Department of Architecture and Computer TechnologyUniversity of GranadaSpain
  2. 2.LASEEB-ISR/ISTUniversity of LisbonPortugal

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