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Coevolutionary Principles

  • Elena Popovici
  • Anthony Bucci
  • R. Paul Wiegand
  • Edwin D. De Jong

Abstract

Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by optimization or evolutionary algorithms. The purpose of this chapter is to provide a foundational understanding of coevolutionary algorithms and to highlight critical theoretical and empirical work done over the last two decades. This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it.

Keywords

Nash Equilibrium Potential Solution Solution Concept Interactive Domain Asymmetric Role 
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 2012

Authors and Affiliations

  1. 1.Icosystem CorporationCambridge, MAUSA
  2. 2.Icosystem CorporationCambridgeUSA
  3. 3.Institute for Simulation and TrainingUniversity of Central FloridaOrlandoUSA
  4. 4.Institute of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands

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