Genetic Algorithm Based-On the Quantum Probability Representation

Authors:
Bin Li;Zhen-quan Zhuang
Affiliations:
-;-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

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Abstract

A genetic algorithm based on the quantum probability representation (GAQPR) is proposed, in which each individual evolves independently; a new crossover operator is designed to integrate searching processes of multiple individuals into a more efficient global searching process; a new mutation operator is also proposed and analyzed. Optimization capability of GAQPR is studied via experiments on function optimization, results of experiments show that, for multi-peak optimization problem, GAQPR is more efficient than GQA[4].