免疫系を用いた遺伝的プログラミングによる多峰性探索

抄録

Artificial Immune System has been regarded an effective powerful optimization framework because of its powerful information processing capabilities. Natural immune system has many features such as memorizing ability, singularity against antigens, flexibility against dynamically changing environments, and diversity of antibody. Up to now, several algorithms inspired by these immune features have been proposed and applied to many problems. However, Genetic Programming with immune features which is capable of solving multimodal problems has not been proposed. This paper proposes an optimization algorithm named Multimodal Search Genetic Programming (MSGP), which extends GP by introducing the immunological feature so as to solve the problems with multimodal fitness landscape. We empirically show the effectiveness of our approach by applying the algorithm to the gene classification problem and the HP protein folding problem.