Application of Adaptive Genetic Algorithm in Function Optimization

The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

• Panpan Xu
• Shulin Sui

• Genetic algorithm
• Adaptive genetic algorithm
• Function optimization.

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