Abstract
The appropriate choice of crossover and mutation rates is critical to the success of genetic algorithms. Earlier researches focused on finding optimal non-dynamic crossover or mutation rates, which vary for different problems, and different stages of the genetic process in a problem. This paper proposed an ameliorate adaptive genetic algorithm where the mutation and crossover rates are adapted dynamically based on the evaluation results of the respective offspring in the next generation. Simultaneously, we combined this new algorithm with PD controllers to improve the stability of control systems. The experimental results are compared with other researchers’ approaches in this field. The PD-GA method can also significantly accelerate the system response and induce lower overshoot as well.
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