Evolving cellular automata by parallel quantum genetic algorithm

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.