Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes

Genetic algorithms are adaptive methods based on natural evolution which may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover and mutation. Under their initial formulation, the search space solutions are coded using the binary alphabet, however other coding types have been taken into account for the representation issue, such as real coding. It seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A great problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of population diversity. The mutation operator is the one responsible for the generation of diversity and therefore may be considered to be an important element in solving this problem. For the case of working under real coding, a solution involves the control, throughout the run, of the strength in which real ge..