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Using PCA to improve evolutionary cellular automata algorithms

Authors:

Mehran Najafi,

Hamid BeigyAuthors Info & Claims

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

Pages 1129 - 1130

https://doi.org/10.1145/1389095.1389312

Published: 12 July 2008 Publication History

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Abstract

The difficulty of designing cellular automatons' transition rules to perform a particular problem has severely limited their applications. Using a genetic algorithm to evolve cellular automata for fining these rules, is a good solution. Conventional evolutionary methods use random test configurations for calculating fitness values of each transition rule. In this paper, we use Principal Component Analysis to build better test configurations. By emphasizing on diversity between test instances in a test plan, we can evaluate rules better and faster as well as increase their accuracy. In this paper, we propose two models based on this idea. Experimental results on density classification and synchronization tasks prove that our methods are more efficient than the conventional one.

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Handa H(2011)Dimensionality reduction of scene and enemy information in Mario2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949795(1515-1520)Online publication date: Jun-2011
https://doi.org/10.1109/CEC.2011.5949795
Handa H(2010)Experimental analysis of the effect of dimensionality reduction on instance-based policy optimizationProceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence10.5555/1884293.1884336(433-444)Online publication date: 30-Aug-2010
https://dl.acm.org/doi/10.5555/1884293.1884336
Handa H(2010)Experimental Analysis of the Effect of Dimensionality Reduction on Instance-Based Policy OptimizationPRICAI 2010: Trends in Artificial Intelligence10.1007/978-3-642-15246-7_40(433-444)Online publication date: 2010
https://doi.org/10.1007/978-3-642-15246-7_40

Index Terms

Using PCA to improve evolutionary cellular automata algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

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Published In

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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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2008

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July 12 - 16, 2008

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Handa H(2011)Dimensionality reduction of scene and enemy information in Mario2011 IEEE Congress of Evolutionary Computation (CEC)10.1109/CEC.2011.5949795(1515-1520)Online publication date: Jun-2011
https://doi.org/10.1109/CEC.2011.5949795
Handa H(2010)Experimental analysis of the effect of dimensionality reduction on instance-based policy optimizationProceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence10.5555/1884293.1884336(433-444)Online publication date: 30-Aug-2010
https://dl.acm.org/doi/10.5555/1884293.1884336
Handa H(2010)Experimental Analysis of the Effect of Dimensionality Reduction on Instance-Based Policy OptimizationPRICAI 2010: Trends in Artificial Intelligence10.1007/978-3-642-15246-7_40(433-444)Online publication date: 2010
https://doi.org/10.1007/978-3-642-15246-7_40

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Evolutionary Cellular Automata Based-Approach for Edge Detection

Evolving evolutionary algorithms using evolutionary algorithms

Evolutionary algorithms for the design of orthogonal latin squares based on cellular automata

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