Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient

IEEE Transactions on Evolutionary Computation, vol. 25, pp. 666–680

Abstract

Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. However, the determination process for the most suitable parameter setting is troublesome and time consuming. Adaptive control parameter methods that can adapt to problem landscape and optimization environment are more preferable than fixed parameter settings. This article proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems. In the approach, the parameter control is modeled as a finite-horizon Markov decision process. A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i.e., parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure. The differential evolution algorithm based on the learned agent is compared against nine well-known evolutionary algorithms on the CEC'13 and CEC'17 test suites. Experimental results show that the proposed algorithm performs competitively against these compared algorithms on the test suites.

Authors 4

1. Jianyong Sun

   Xi'an Jiaotong University

   Affiliation as printed

   School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

   School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China

2. Xin Liu

   Xi'an Jiaotong University

   Affiliation as printed

   School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

   School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China

3. Thomas Bäck Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, Leiden, RA, The Netherlands

4. Zongben Xu

   Xi'an Jiaotong University

   Affiliation as printed

   School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

   School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China