research-article

An Improved Algorithm for Solving Scheduling Problems by Combining Generative Adversarial Network with Evolutionary Algorithms

Authors:

Zhihua YuAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 10, Pages 1 - 7

https://doi.org/10.1145/3331453.3361639

Published: 22 October 2019 Publication History

Abstract

With1 the continuous application of evolutionary algorithms in various combinatorial optimization problems, the traditional evolutionary algorithms are prone to premature convergence and fall into local optimization solutions as the complexity of the problems increases. To solve this problem, this paper proposes a hybrid algorithm combining the Generative adversarial nets (GAN) and Genetic Algorithm (GA). The algorithm is based on Genetic Algorithm and introducted the GAN sample as another sample to the generated model. The algorithm expected more abundant sample information through GAN mining, got the advantage of sample training GAN through the GA. It makes GAN learn from the edge of sample information, which can generate more advantages of samples. The generated sample is injected into the evolution of the next generation, increasing the diversity of samples and increasing the opportunity to find the optimal solution. In this paper, the hybrid algorithm is used to solve the Permutation Flow Shop Problem to verify the algorithm's solution ability. Experimental results show that the hybrid algorithm can avoid premature local optimal solution compared with the traditional evolutionary algorithm.

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Digital Library

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Cited By

Wang YZhang QWang GCheng H(2024)The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature surveyArtificial Intelligence Review10.1007/s10462-024-10818-y57:7Online publication date: 21-Jun-2024
https://doi.org/10.1007/s10462-024-10818-y
Bu HHu CYuan XJi XLyu HZhou H(2023)An Image Generation Method of Unbalanced Ship Coating Defects Based on IGASEN-EMWGANCoatings10.3390/coatings1303062013:3(620)Online publication date: 14-Mar-2023
https://doi.org/10.3390/coatings13030620
Yu RWen HXu Y(2022)Evolutionary Discrete Optimization Inspired by Zero-Sum Game Theory2022 18th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN57253.2022.00159(984-989)Online publication date: Dec-2022
https://doi.org/10.1109/MSN57253.2022.00159
Show More Cited By

Index Terms

An Improved Algorithm for Solving Scheduling Problems by Combining Generative Adversarial Network with Evolutionary Algorithms
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Other conferences

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Wang YZhang QWang GCheng H(2024)The application of evolutionary computation in generative adversarial networks (GANs): a systematic literature surveyArtificial Intelligence Review10.1007/s10462-024-10818-y57:7Online publication date: 21-Jun-2024
https://doi.org/10.1007/s10462-024-10818-y
Bu HHu CYuan XJi XLyu HZhou H(2023)An Image Generation Method of Unbalanced Ship Coating Defects Based on IGASEN-EMWGANCoatings10.3390/coatings1303062013:3(620)Online publication date: 14-Mar-2023
https://doi.org/10.3390/coatings13030620
Yu RWen HXu Y(2022)Evolutionary Discrete Optimization Inspired by Zero-Sum Game Theory2022 18th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN57253.2022.00159(984-989)Online publication date: Dec-2022
https://doi.org/10.1109/MSN57253.2022.00159
Lemtenneche SCheriet ABensayah A(2022)Introducing Generative adversarial networks on Estimation of distribution algorithm to solve permutation-based problems2022 International Symposium on iNnovative Informatics of Biskra (ISNIB)10.1109/ISNIB57382.2022.10075720(1-5)Online publication date: 7-Dec-2022
https://doi.org/10.1109/ISNIB57382.2022.10075720
Yu RXu YPeng HChen M(2022)Enhancing Population Diversity by Integrating Iterative Local Search with Deep Convolutional Generative Adversarial Networks (GANs)2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956322(4722-4728)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956322

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