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Why Coevolution Doesn’t “Work”: Superiority and Progress in Coevolution

  • Thomas Miconi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

Coevolution often gives rise to counter-intuitive dynamics that defy our expectations. Here we suggest that much of the confusion surrounding coevolution results from imprecise notions of superiority and progress. In particular, we note that in the literature, three distinct notions of progress are implicitly lumped together: local progress (superior performance against current opponents), historical progress (superior performance against previous opponents) and global progress (superior performance against the entire opponent space). As a result, valid conditions for one type of progress are unduly assumed to lead to another. In particular, the confusion between historical and global progress is a case of a common error, namely using the training set as a test set. This error is prevalent among standard methods for coevolutionary analysis (CIAO, Master Tournament, Dominance Tournament, etc.) By clearly defining and distinguishing between different types of progress, we identify limitations with existing techniques and algorithms, address them, and generally facilitate discussion and understanding of coevolution. We conclude that the concepts proposed in this paper correspond to important aspects of the coevolutionary process.

Keywords

Nash Equilibrium Solution Concept Local Progress Dominant Individual Pareto Dominance 
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 2009

Authors and Affiliations

  • Thomas Miconi
    • 1
  1. 1.School of Computer ScienceUniversity of BirminghamBirminghamUK

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