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Fitnessless coevolution

Published: 12 July 2008 Publication History

Abstract

We introduce fitnessless coevolution (FC), a novel method of comparative one-population coevolution. FC plays games between individuals to settle tournaments in the selection phase and skips the typical phase of evaluation. The selection operator applies a single-elimination tournament to a randomly drawn group of individuals, and the winner of the final round becomes the result of selection. Therefore, FC does not involve explicit fitness measure. We prove that, under a condition of transitivity of the payoff matrix, the dynamics of FC is identical to that of the traditional evolutionary algorithm. The experimental results, obtained on a diversified group of problems, demonstrate that FC is able to produce solutions that are equally good or better than solutions obtained using fitness-based one-population coevolution with different selection methods.

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Cited By

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  • (2023)GPStar4: A flexible framework for experimenting with genetic programmingProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596369(1910-1915)Online publication date: 15-Jul-2023
  • (2022)Overtaking Uncertainty With Evolutionary TORCS Controllers: Combining BLX With Decreasing $\alpha$ Operator and Grand Prix SelectionIEEE Transactions on Games10.1109/TG.2021.307241714:2(318-327)Online publication date: Jun-2022
  • (2021)Competitive coevolution for defense and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3463193(1898-1906)Online publication date: 7-Jul-2021
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cover image ACM Conferences
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation
July 2008
1814 pages
ISBN:9781605581309
DOI:10.1145/1389095
  • Conference Chair:
  • Conor Ryan,
  • Editor:
  • Maarten Keijzer
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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Author Tags

  1. games
  2. one-population coevolution
  3. selection methods

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Cited By

View all
  • (2023)GPStar4: A flexible framework for experimenting with genetic programmingProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596369(1910-1915)Online publication date: 15-Jul-2023
  • (2022)Overtaking Uncertainty With Evolutionary TORCS Controllers: Combining BLX With Decreasing $\alpha$ Operator and Grand Prix SelectionIEEE Transactions on Games10.1109/TG.2021.307241714:2(318-327)Online publication date: Jun-2022
  • (2021)Competitive coevolution for defense and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3463193(1898-1906)Online publication date: 7-Jul-2021
  • (2017)Accelerating coevolution with adaptive matrix factorizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071320(457-464)Online publication date: 1-Jul-2017
  • (2017)Development of a Co-evolutionary Radial Basis Function Neural Classifier by a k-Random Opponents TopologyEmerging Trends in Neuro Engineering and Neural Computation10.1007/978-981-10-3957-7_11(207-217)Online publication date: 25-Mar-2017
  • (2013)Evolving Tic-Tac-Toe Playing Algorithms Using Co-Evolution, Interactive Fitness and Genetic ProgrammingInternational Journal of Computer Theory and Engineering10.7763/IJCTE.2013.V5.799(797-801)Online publication date: 2013
  • (2012)Coevolutionary PrinciplesHandbook of Natural Computing10.1007/978-3-540-92910-9_31(987-1033)Online publication date: 2012
  • (2008)Evolving strategy for a probabilistic game of imperfect information using genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-008-9062-19:4(281-294)Online publication date: 1-Dec-2008

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