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Evolving cellular automata by parallel quantum genetic algorithm

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2 Author(s)
Zakaria Laboudi ; SCAL group, MISC Laboratory Mentouri University ¿ Route de Aïn El Bey 25017 Constantine. Algeria ; Salim Chikhi

Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary algorithms was invented: quantum genetic algorithms. In this paper, we outline the approach of quantum genetic algorithm (QGA) by giving an example where it serves to automatically program cellular automata (CA) rules. Our results have shown that QGA can be a very promising tool for exploring CA search spaces.

Published in:

2009 First International Conference on Networked Digital Technologies

Date of Conference:

28-31 July 2009