Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient

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 p...

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Vydáno v:	IEEE transactions on evolutionary computation Ročník 25; číslo 4; s. 666 - 680
Hlavní autoři:	Sun, Jianyong, Liu, Xin, Back, Thomas, Xu, Zongben
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptive algorithms Adaptive control Adaptive differential evolution Algorithms Convergence Deep learning Evolutionary algorithms Evolutionary computation global optimization Machine learning Markov processes Mutation Optimization policy gradient (PG) Process control Process parameters Reinforcement learning reinforcement learning (RL) Sociology Statistics
ISSN:	1089-778X, 1941-0026
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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.
AbstractList	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.
Author	Back, Thomas Xu, Zongben Liu, Xin Sun, Jianyong
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Snippet	Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a...
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SubjectTerms	Adaptive algorithms Adaptive control Adaptive differential evolution Algorithms Convergence Deep learning Evolutionary algorithms Evolutionary computation global optimization Machine learning Markov processes Mutation Optimization policy gradient (PG) Process control Process parameters Reinforcement learning reinforcement learning (RL) Sociology Statistics
Title	Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient
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