Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Transactions on Evolutionary Computation

سال: 2021

ISSN: ['1941-0026', '1089-778X']

DOI: https://doi.org/10.1109/tevc.2021.3060811