Genetik algoritmalar için assortatif- disassortatif seçilim mekanizmalari

The aim of this study is to improve the performance of Genetic Algorithm (GA) and extend the GA towards a more natural approach by incorporating assortative & disassortative mating (ADM) to the selection strategies. In this study, a simple and efficient ADM based real-coded genetic algorithm (RCGA) is proposed and then employed to solve complex function optimization problems. The suggested DISASSORTATIVE mating approaches enhances the abilities of GAs in searching global optima as well as in speeding convergence by integrating the ASSORTATIVE mating search strategies. Eight different ADM strategies were proposed within this study. Using ten benchmark global optimization test functions, the performance of these strategies were evaluated. Results indicate that the disassortative based mating strategies are fast, accurate, and reliable, and outperform all the other GAs considered in the present study. Key Words: Genetic algorithm, diversity, disassortative mating, assortative mating.