Reducing execution time on genetic algorithm in real-world applications using fitness prediction: parameter optimization of SRM control

Genetic algorithm (GA) is an effective method of solving combinatorial optimization problems. Generally speaking most of search algorithms require a large execution time in order to calculate some evaluation value, especially in real-world applications as well. Crossover is very important in GA because discovering a good solution efficiently requires that the good characteristics of the parent individuals be recombined. The multiple crossover per couple (MCPC) is a method that permits a number of children for each mating pair, and MCPC generates a huge amount of execution time to find a good solution. This paper proposes a novel approach to reduce time needed for fitness evaluation by "prenatal diagnosis" using fitness prediction. In the experiments based on actual problems, the proposed method found an optimum solution about 50% faster than the conventional method did. The experimental results from standard test functions show that the proposed method is applicable to other problem as well.