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Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient
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DOI：10.1109/TEVC.2021.3060811.png)
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Abstract
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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.
Keywords:
Statistics
Sociology
Optimization
Process control
Deep learning
Reinforcement learning
Convergence
Adaptive differential evolution
deep learning
global optimization
policy gradient (PG)
reinforcement learning (RL)
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