Article

Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control

Authors:

Forrest H Bennett, III,

Eleanor G. RieffelAuthors Info & Claims

SAC '02: Proceedings of the 2002 ACM symposium on Applied computing

Pages 553 - 559

https://doi.org/10.1145/508791.508899

Published: 11 March 2002 Publication History

Abstract

We investigate two techniques for co-evolving and sampling from a population of fitness cases, and compare these with a random sampling technique. We design three symbolic regression problems on which to test these techniques, and also measure their relative performance on a modular robot control problem. The methods have varying relative performance, but in all of our experiments, at least one of the co-evolutionary methods outperforms the random sampling method by guiding evolution, with substantially fewer fitness evaluations, toward solutions that generalize best on an out-of-sample test set.

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Cited By

Drahosova MSekanina LWiglasz M(2019)Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic ProgrammingEvolutionary Computation10.1162/evco_a_0022927:3(497-523)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1162/evco_a_00229
Agapitos ALoughran RNicolau MLucas SO’Neill MBrabazon A(2019)A Survey of Statistical Machine Learning Elements in Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.290091623:6(1029-1048)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1109/TEVC.2019.2900916
Rodriguez-Coayahuitl LMorales-Reyes AEscalante H(2019)Evolving autoencoding structures through genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-019-09354-420:3(413-440)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10710-019-09354-4
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Co-evolving an effective fitness sample: experiments in symbolic regression and distributed robot control
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Published In

cover image ACM Conferences

SAC '02: Proceedings of the 2002 ACM symposium on Applied computing

March 2002

1200 pages

ISBN:1581134452

DOI:10.1145/508791

Conference Chair:
Gary B. Lamont
Air Force Institute of Technology, USA
,
Program Chairs:
Hisham Haddad
Kennesaw State Univ., USA
,
George Papadopoulos
Univ. of Cyprus, Cyprus
,
Publications Chair:
Brajendra Panda
University of Arkansas, USA

Copyright © 2002 ACM.

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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Published: 11 March 2002

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March 11 - 14, 2002

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Cited By

Drahosova MSekanina LWiglasz M(2019)Adaptive Fitness Predictors in Coevolutionary Cartesian Genetic ProgrammingEvolutionary Computation10.1162/evco_a_0022927:3(497-523)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1162/evco_a_00229
Agapitos ALoughran RNicolau MLucas SO’Neill MBrabazon A(2019)A Survey of Statistical Machine Learning Elements in Genetic ProgrammingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.290091623:6(1029-1048)Online publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1109/TEVC.2019.2900916
Rodriguez-Coayahuitl LMorales-Reyes AEscalante H(2019)Evolving autoencoding structures through genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-019-09354-420:3(413-440)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10710-019-09354-4
LIU QODAKA TKUROIWA JOGURA H(2013)Application of an Artificial Fish Swarm Algorithm in Symbolic RegressionIEICE Transactions on Information and Systems10.1587/transinf.E96.D.872E96.D:4(872-885)Online publication date: 2013
https://doi.org/10.1587/transinf.E96.D.872
Šikulová MSekanina L(2012)Coevolution in cartesian genetic programmingProceedings of the 15th European conference on Genetic Programming10.1007/978-3-642-29139-5_16(182-193)Online publication date: 11-Apr-2012
https://dl.acm.org/doi/10.1007/978-3-642-29139-5_16
Lu QWang B(2011)Notice of Retraction Feature fitness evaluation for symbolic regression via genetic programming2011 Seventh International Conference on Natural Computation10.1109/ICNC.2011.6022150(1087-1091)Online publication date: Jul-2011
https://doi.org/10.1109/ICNC.2011.6022150
Schmidt MLipson H(2008)Coevolution of Fitness PredictorsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2008.91900612:6(736-749)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1109/TEVC.2008.919006
Greenfield G(2008)Co-evolutionary Methods in Evolutionary ArtThe Art of Artificial Evolution10.1007/978-3-540-72877-1_17(357-380)Online publication date: 2008
https://doi.org/10.1007/978-3-540-72877-1_17
Schmidt MLipson H(2007)Coevolving Fitness Models for Accelerating Evolution and Reducing EvaluationsGenetic Programming Theory and Practice IV10.1007/978-0-387-49650-4_8(113-130)Online publication date: 2007
https://doi.org/10.1007/978-0-387-49650-4_8
Bongard JLipson HO'Reilly U(2005)'Managed challenge' alleviates disengagement in co-evolutionary system identificationProceedings of the 7th annual conference on Genetic and evolutionary computation10.1145/1068009.1068097(531-538)Online publication date: 25-Jun-2005
https://dl.acm.org/doi/10.1145/1068009.1068097

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