A comparative study of evolutionary optimization techniques in dynamic environments

Genetic Algorithms have widely been used for solving optimization problems in stationary environments. In recent years, there has been a growing interest for investigating and improving the performance of these algorithms in dynamic environments where the fitness landscape changes. In this study, we present an extensive comparison of several algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying problem parameters