Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models

Abstract

This paper proposes a novel method Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM) such as estimation of distribution algorithms and cross entropy methods.In HIS multiple populations are maintained simultaneously such that they have different diversities and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM.This paper proposes a novel method, Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM) such as estimation of distribution algorithms and cross entropy methods.In HIS, multiple populations are maintained simultaneously such that they have different diversities, and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM.