An evolutionary algorithm based on stochastic weighted learning for continuous optimization

Abstract

In this paper, we propose an evolutionary algorithm based on a single operator called stochastic weighted learning for continuous optimization. Unlike most other EAs that have different selection strategies, mutation rules and crossover operators, the proposed algorithm uses only one operator that mimics the strategy learning process of rational economic agents, i.e., each agent in a population update its strategy to improve its fitness by learning from other agents' strategies specified with stochastic weight coefficients, to achieve the objective of optimization. Experiment results on several optimization problems and comparisons with other evolutionary algorithms show the efficiency of the proposed algorithm.