Using PCA to improve evolutionary cellular automata algorithms

Authors:
Mehran Najafi;Hamid Beigy
Affiliations:
McMaster University, Hamilton, ON, Canada;Sharif University of Technology, Tehran, Iran
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 3
Cited 2

Evolving cellular automata to perform computations: mechanisms and impediments

Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
A new kind of science

A new kind of science
An Efficient k-Means Clustering Algorithm: Analysis and Implementation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Experimental analysis of the effect of dimensionality reduction on instance-based policy optimization

PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Neuroevolution with manifold learning for playing Mario

International Journal of Bio-Inspired Computation

Quantified Score

Hi-index	0.00

Visualization

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.