research-article

Evolution of cartesian genetic programs capable of learning

Authors:

Gul Muhammad Khan,

Julian F. MillerAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 707 - 714

https://doi.org/10.1145/1569901.1569999

Published: 08 July 2009 Publication History

Abstract

We propose a new form of Cartesian Genetic Programming (CGP) that develops into a computational network capable of learning. The developed network architecture is inspired by the brain. When the genetically encoded programs are run, a networks develops consisting of neurons, dendrites, axons, and synapses which can grow, change or die. We have tested this approach on the task of learning how to play checkers. The novelty of the research lies mainly in two aspects: Firstly, chromosomes are evolved that encode programs rather than the network directly and when these programs are executed they build networks which appear to be capable of learning and improving their performance over time solely through interaction with the environment. Secondly, we show that we can obtain learning programs much quicker through co-evolution in comparison to the evolution of agents against a minimax based checkers program. Also, co-evolved agents show significantly increased learning capabilities compared to those that were evolved to play against a minimax-based opponent.

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Cited By

Risi STogelius J(2017)Neuroevolution in Games: State of the Art and Open ChallengesIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2015.24945969:1(25-41)Online publication date: Mar-2017
https://doi.org/10.1109/TCIAIG.2015.2494596
Miller J(2014)Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural NetworksGrowing Adaptive Machines10.1007/978-3-642-55337-0_8(227-249)Online publication date: 5-Jun-2014
https://doi.org/10.1007/978-3-642-55337-0_8
Miller JKhan G(2011)Where is the brain inside the brain?Memetic Computing10.1007/s12293-011-0062-y3:3(217-228)Online publication date: 17-Jun-2011
https://doi.org/10.1007/s12293-011-0062-y
Show More Cited By

Index Terms

Evolution of cartesian genetic programs capable of learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 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: 08 July 2009

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Risi STogelius J(2017)Neuroevolution in Games: State of the Art and Open ChallengesIEEE Transactions on Computational Intelligence and AI in Games10.1109/TCIAIG.2015.24945969:1(25-41)Online publication date: Mar-2017
https://doi.org/10.1109/TCIAIG.2015.2494596
Miller J(2014)Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural NetworksGrowing Adaptive Machines10.1007/978-3-642-55337-0_8(227-249)Online publication date: 5-Jun-2014
https://doi.org/10.1007/978-3-642-55337-0_8
Miller JKhan G(2011)Where is the brain inside the brain?Memetic Computing10.1007/s12293-011-0062-y3:3(217-228)Online publication date: 17-Jun-2011
https://doi.org/10.1007/s12293-011-0062-y
Azad RRyan CPelikan MBranke J(2010)Abstract functions and lifetime learning in genetic programming for symbolic regressionProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830645(893-900)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830645

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