Genetic Evolution of Control Systems.

In this paper, we present to utilize Genetic Algorithms (GAs) as tools to model control processes. Two different crossover operators are combined during evolution to maintain population diversity and to sustain local improvement in the search space. In this manner, a balance between global exploration and local exploitation is reserved during genetic search. To verify the efficiency of the proposed method, the desired control sequences of a given system are solved by the optimal control theory as well as GA with hybrid crossovers to compare their performances. The experimental results showed that the control sequences obtained from the proposed GA with hybrid crossovers are quite consistent with the results of the optimal control.