论文信息 - Intelligent Agent-Inspired Genetic Algorithm

Intelligent Agent-Inspired Genetic Algorithm

This paper presents an intelligent agent-inspired genetic algorithm (IAGA). Analogous to the intelligent agent, each individual in IAGA has its own properties, including crossover probability, mutation probability, etc. Numerical simulations demonstrate that, compared with the standard GA where all individuals in a population share the same crossover and mutation probabilities, the proposed algorithm is more flexible, efficient and effective.

Yanchun Liang | C. G. Wu | H. P. Lee | C. Lu

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