Relative Fitness and Absolute Fitness for Co-evolutionary Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{eurogp:JinT05,
  author =       "Nanlin Jin and Edward P. K. Tsang",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Relative Fitness and Absolute Fitness for
                 Co-evolutionary Systems",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "331--340",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "The commonly adopted fitness which evaluates the
                 performance of individuals in co-evolutionary systems
                 is relative fitness. Relative fitness is a dynamic
                 assessment subject to the other co-evolving
                 population(s). Researchers apparently pay less
                 attention to the use of absolute fitness functions in
                 studying co-evolutionary algorithms than the use of
                 relative fitness functions. One of our aims in this
                 work is to formalise both relative fitness and absolute
                 fitness for co-evolving systems. Another aim is to
                 demonstrate the usage of absolute and relative fitness
                 through a case study. We develop a co-evolutionary
                 system by means of Genetic Programming to discover
                 co-adapted strategies for a Basic Alternating-Offers
                 Bargaining Problem. In this case, the relative fitness
                 essentially drives co-evolution to converge to
                 game-theoretic equilibrium. Whereas the relative
                 fitness alone can not discover the whole view of
                 co-evolutionary progress. The absolute fitness, on the
                 other hand helps us to monitor the development of
                 co-adaptive learning. Having analysed the
                 micro-behaviour of the players' strategies, based on
                 their absolute fitness, we can explain how the
                 co-evolving populations converge to the perfect
                 equilibria.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Nanlin Jin Edward P K Tsang

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