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Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

He, Cheng, Huang, Shihua, Cheng, Ran, Tan, Kay Chen and Jin, Yaochu (2020) Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs) IEEE Transactions on Cybernetics. pp. 1-14.

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Abstract

Recently, increasing works have been proposed to drive evolutionary algorithms using machine-learning models. Usually, the performance of such model-based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e., the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales due to the curse of dimensionality. To address this issue, we propose a multiobjective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on ten benchmark problems with up to 200 decision variables. The experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
He, Cheng
Huang, Shihua
Cheng, Ran
Tan, Kay Chen
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 30 April 2020
DOI : 10.1109/TCYB.2020.2985081
Copyright Disclaimer : © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Deep learning; Machine learning; Evolutionary algorithm; Generative adversarial networks (GANs); Machine learning; Multiobjective optimization
Depositing User : Clive Harris
Date Deposited : 13 May 2020 15:49
Last Modified : 13 May 2020 15:50
URI: http://epubs.surrey.ac.uk/id/eprint/856401

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