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On the Run-Time Dynamics of a Peer-to-Peer Evolutionary Algorithm

  • Juan L. J. Laredo
  • Agoston E. Eiben
  • Maarten van Steen
  • Juan J. Merelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

In this paper we propose an improvement on a fully distributed Peer-to-Peer (P2P) Evolutionary Algorithm (EA) based on autonomous selection. Autonomous selection means that individuals decide on their own state of reproduction and survival without any central control, using instead estimations about the global population state for decision making. The population size varies at run-time as a consequence of such a decentralized reproduction and death of individuals. In order to keep it stable, we propose a self-adjusting mechanism which has been shown successful in three different search landscapes. Key are the estimations about fitness and size of the population as provided by a gossiping algorithm. Such an algorithm requires several rounds to collect the information while the individuals have to wait for synchronization. As an improvement, we propose a completely asynchronous EA which does not need waiting times. The results show that our approach outperforms quantitatively the execution time of the synchronous version.

Keywords

Evolutionary Algorithm Tournament Selection Initial Population Size Counting Algorithm Idle Cycle 
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
  • Agoston E. Eiben
    • 2
  • Maarten van Steen
    • 2
  • Juan J. Merelo
    • 1
  1. 1.Department of Architecture and Computer TechnologyUniversity of GranadaSpain
  2. 2.Department of Computer ScienceVrije Universiteit AmsterdamThe Netherlands

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