Biologically inspired methods for control of evolutionary algorithms

In this paper two methods for evolutionary algorithm control are proposed. The first one is a new method of tuning tlie probabilities of genetic operators. It is assumed in the presented approach that every member of the optimized population conducts his own ranking of genetic operators' qualities. This ranking enables computing the probabilities of execution of genetic operators. This set of probabilities is a basis of experience of every individual and according to this basis the individual chooses the operator in every iteration of the algorithm. Due to this experience one can maximize the chances of his offspring to survive. The second part of the paper deals with a self-adapting method of selection of individuals to a subsequent generation. Methods of selection applied in the evolutionary algorithms are usually inspired by nature and prefer solutions where the main role is played by randomness, competition and struggle among individuals. In the case of evolutionary algorithms, where populations of individuals are usually small, this causes a premature convergence to local minima. In order to avoid this drawback I propose to apply an approach based rather on an agricultural technique. Two new methods of object selection are proposed: a histogram selection and a mixed selection. The methods described were tested using examples based on scheduling and TSP