Self-adaptation in genetic algorithms

A self-adaptation seems to be a promising way of genetic algorithms, where the parameters of the genetic algorithm are optimized during the same evolution cycle as the problem itself. The aim of this paper is to present a new encoding for self-adaptation of genetic algorithms. Comparing to previous approaches we designed the encoding for self-adaptation not only one or several but all possible parameters of genetic algorithms at the same time. The proposed self-adapting genetic algorithm is compared to standard genetic algorithms on several test problems. The main advantage of our approach is that it enables to solve wide range of optimization problems without setting parameters for each kind of problem in advance.