Adaptation to a dynamic environment by means of the environment identifying genetic algorithm

Adaptation. to dynamic environments is an important application of genetic algorithms (GAs). However, there are many difficulties in applying a GA to dynamic environments. In particular, in online environments, the GA's defects become remarkable because individuals should be evaluated in the real world. In this paper, we propose a novel approach to such an online adaptation, called the "environment-identifying genetic algorithm" (EIGA). EIGA achieves the online adaptation and identification of the environment simultaneously by a parallel technique and reduces the number of fitness evaluations in the real world by utilizing the identified environment. A thermodynamic selection rule is also utilized to maintain diversity. A computer simulation is carried out by taking an Nk-landscape problem as an example.