An evolutionary approach for time dependant optimization

Many real-world problems involve measures of objectives that may be dynamically optimized. The application of evolutionary algorithms in time dependent optimization is receiving growing interest as potential applications are numerous ranging from mobile robotics to real time process command. Moreover, constant evaluation functions skew results relative to natural evolution so that it has become a promising gap to combine effectiveness and diversity in a genetic algorithm. This paper features both theoretical and empirical analysis of the behavior of genetic algorithms in such an environment. It presents a comparison between the effectiveness of traditional sample genetic algorithm (SGA) and the dual genetic algorithm (DGA) which is revealed to be a particularly adaptive tool for optimizing a lot of diversified classes of functions. This comparison has been performed on a model of a dynamical environment. Its characteristics are analyzed in order to establish the basis of a testbed for further experiments. We discuss fundamental properties that explain the effectiveness of the dual paradigm to manage dynamical environments.