Memory and Prediction Based Genetic Algorithm Using Speciation in Dynamic Multimodal Function Optimization

Abstract

It is a difficult problem for Evolutionary Algorithms to search an optimal solution in multimodal functions with dynamic environments, where individuals search for more than one optima and their fitness value changes over time 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 for an optimal solutions in multimodal functions.