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]
The research is supported by the National Natural Science Foundation of China under Grant No. 60171029.
Bin LI is currently a visiting scholar at Information Systems Institute, Technical University of Vienna, Austria.
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Li, B., Zhuang, Zq. (2002). Genetic Algorithm Based-On the Quantum Probability Representation. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_75
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DOI: https://doi.org/10.1007/3-540-45675-9_75
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