The convergence prediction method for genetic and PBIL-like algorithms with binary representation

Genetic algorithms (GA) are stochastic search procedures which have been used for solving many complex optimization problems. It is obvious that GA collect and exploit some statistical information about the search space, but this information isn't processed in explicit way. In this paper, we consider GA with binary representation and its explicit statistics in a form of probability distribution of unit-values. The binary GA convergence property is discussed and a new convergence prediction method is proposed. The results of the prediction algorithm effectiveness investigation over the set of complex continuous and discrete deceptive problems are presented.