Relative Fitness and Absolute Fitness for Co-evolutionary Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{eurogp:JinT05,
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author = "Nanlin Jin and Edward P. K. Tsang",
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editor = "Maarten Keijzer and Andrea Tettamanzi and
Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
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title = "Relative Fitness and Absolute Fitness for
Co-evolutionary Systems",
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booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "3447",
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year = "2005",
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address = "Lausanne, Switzerland",
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month = "30 " # mar # " - 1 " # apr,
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organisation = "EvoNet",
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keywords = "genetic algorithms, genetic programming: Poster",
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ISBN = "3-540-25436-6",
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pages = "331--340",
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DOI = "doi:10.1007/978-3-540-31989-4_30",
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DOI = "doi:10.1007/b107383",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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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.",
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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
Citations