Text

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 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.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.25 No.4 August 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 666 - 680

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENTUK GALAKSI
GLOBAL OPTIMIZATION
REINFORCEMENT LEARNING (RL)
ADAPTIVE DIFFERENTIAL EVOLUTION
POLICY GRADIENT (PG)

Info Detail Spesifik

DOI: 10.1109/TEVC.2021.3060811

Pernyataan Tanggungjawab

Jianyong Sun, Xin Liu, Thomas Back, Zongben Xu

Versi lain/terkait

Lampiran Berkas

Komentar