メモリと予測を用いた種分化を行う遺伝的アルゴリズムによる動的に変化する多峰性問題の最適化

摘要

It is difficult problems for Evolutionary Algorithms to search an optimal solution in multimodal functions with dynamic environments, where individuals search more than one optima and their fitness value changes under such environments. In this paper we propose a method of Memory and Prediction Based Genetic Algorithm Using speciation. This method is extended with a case-based memory and a meta-learner for precise prediction of environmental change. Especially，the individuals in a memory consist of 4 kinds of predictors and they can adjust to the change of dynamic environment adaptively. To verify the effectiveness，the method is examined to search optimal solutions in multimodal functions.