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A Population-Differential Method of Monitoring Success and Failure in Coevolution

  • Ari Bader-Natal
  • Jordan B. Pollack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3102)

Abstract

Coevolutionary algorithms require no domain-specific measure of objective fitness, enabling these these algorithms to be applied to domains for which no objective metric is known or for which known metrics are too expensive. But this flexibility comes at the expense of accountabilitiy. Past work on monitoring has focused on measuring success, but has not been able to provide feedback on failure. This limitation is due to a common reliance on “best-of-generation” (BOG) based analysis [1], and we propose a population-differential analysis based on an alternate “all-of-generation” (AOG) framework that is not similarly limited.

Keywords

Coevolutionary Algorithm Number Game Objective Fitness Monitoring Success Coevolutionary Dynamic 
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 2004

Authors and Affiliations

  • Ari Bader-Natal
    • 1
  • Jordan B. Pollack
    • 1
  1. 1.DEMO Lab, Computer Science Department MS018Brandeis UniversityWalthamUSA

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