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An empirical comparison of evolution and coevolution for designing artificial neural network game players

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Min ShiAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 379 - 386

https://doi.org/10.1145/1389095.1389164

Published: 12 July 2008 Publication History

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Abstract

In this paper, we compare two neuroevolutionary algorithms, namely standard NeuroEvolution (NE) and NeuroEvolution of Augmenting Topologies (NEAT), with three neurocoevolutionary algorithms, namely Symbiotic Adaptive Neuro-Evolution (SANE), Enforced Sub-Populations (ESP) and Evolving Efficient Connections (EEC). EEC is a novel neurocoevolutionary algorithm that we propose in this work, where the connection weights and the connection paths of networks are evolved separately. All these methods are applied to evolve players of two different board games. The results of this study indicate that neurocoevolutionary algorithms outperform neuroevolutionary algorithms for both domains. Our new method, especially, demonstrates that fully connected networks could generate noise which results in inefficient learning. The performance of standard NE model has been improved significantly through evolving connection weights and efficient connection paths in parallel in our method.

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Shi MHøverstad B(2009)PEECProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689807(1578-1584)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689807
Shi MAnnfelt Hoverstad B(2009)PEEC: Evolving efficient connections using Pareto optimality2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983130(1578-1584)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983130

Index Terms

An empirical comparison of evolution and coevolution for designing artificial neural network game players
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
  2. Machine learning
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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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
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Published: 12 July 2008

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July 12 - 16, 2008

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Shi MHøverstad B(2009)PEECProceedings of the Eleventh conference on Congress on Evolutionary Computation10.5555/1689599.1689807(1578-1584)Online publication date: 18-May-2009
https://dl.acm.org/doi/10.5555/1689599.1689807
Shi MAnnfelt Hoverstad B(2009)PEEC: Evolving efficient connections using Pareto optimality2009 IEEE Congress on Evolutionary Computation10.1109/CEC.2009.4983130(1578-1584)Online publication date: May-2009
https://doi.org/10.1109/CEC.2009.4983130

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Evolving neural networks

Investigating whether hyperNEAT produces modular neural networks

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