Learning adaptive differential evolution algorithm from optimization experiences by policy gradient

• URL: http://arxiv.org/abs/2102.03572v1
• Date: Sat, 6 Feb 2021 12:01:20 GMT
• Title: Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
• Authors: Jianyong Sun and Xin Liu and Thomas B\"ack and Zongben Xu
• Abstract summary: This paper proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems. A reinforcement learning algorithm, named policy, is applied to learn an agent that can provide the control parameters of a proposed differential evolution adaptively. The proposed algorithm performs competitively against nine well-known evolutionary algorithms on the CEC'13 and CEC'17 test suites.
• Score: 24.2122434523704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 paper 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.

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