Learning to Adapt Genetic Algorithms for Multi-Objective Flexible Job Shop Scheduling Problems
Abstract
The configuration of Evolutionary Algorithm (EA) parameters is a significant challenge. While previous studies have examined methods for configuring EA parameters, there remains a lack of a general solution for optimizing these parameters. To overcome this, we propose DEMOCA, an automated Deep Reinforcement Learning (DRL) method for online control of multi-objective EA parameters. When tested on a multi-objective Flexible Job Shop Scheduling Problem (FJSP) using a Genetic Algorithm (GA), DEMOCA was found to be as effective as grid search while requiring significantly less training cost.
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References
[1]
Aldeida Aleti and Irene Moser. 2016. A systematic literature review of adaptive parameter control methods for evolutionary algorithms. ACM Computing Surveys (CSUR) 49, 3 (2016), 1--35.
[2]
Paolo Brandimarte. 1993. Routing and scheduling in a flexible job shop by tabu search. Annals of Operations research 41, 3 (1993), 157--183.
[3]
Carlos A Coello Coello, Gary B Lamont, David A Van Veldhuizen, et al. 2007. Evolutionary algorithms for solving multi-objective problems. Vol. 5. Springer.
[4]
Yann Collette and Patrick Siarry. 2005. Three new metrics to measure the convergence of metaheuristics towards the Pareto frontier and the aesthetic of a set of solutions in biobjective optimization. Computers & operations research 32, 4 (2005), 773--792.
[5]
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182--197.
[6]
Parviz Fattahi, Mohammad Saidi Mehrabad, and Fariborz Jolai. 2007. Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of intelligent manufacturing 18, 3 (2007), 331--342.
[7]
Imed Kacem, Slim Hammadi, and Pierre Borne. 2002. Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 32, 1 (2002), 1--13.
[8]
Giorgos Karafotias, Mark Hoogendoorn, and Ágoston E Eiben. 2014. Parameter control in evolutionary algorithms: Trends and challenges. IEEE Transactions on Evolutionary Computation 19, 2 (2014), 167--187.
[9]
Robbert Reijnen, Yingqian Zhang, Zaharah Bukhsh, and Mateusz Guzek. 2022. Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 804--811.
[10]
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).
[11]
Mudita Sharma, Alexandros Komninos, Manuel López-Ibáñez, and Dimitar Kazakov. 2019. Deep reinforcement learning based parameter control in differential evolution. In Proceedings of the Genetic and Evolutionary Computation Conference. 709--717.
[12]
Jianyong Sun, Xin Liu, Thomas Bäck, and Zongben Xu. 2021. Learning adaptive differential evolution algorithm from optimization experiences by policy gradient. IEEE Transactions on Evolutionary Computation 25, 4 (2021), 666--680.
[13]
Guohui Zhang, Liang Gao, and Yang Shi. 2011. An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications 38, 4 (2011), 3563--3573.
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- Learning to Adapt Genetic Algorithms for Multi-Objective Flexible Job Shop Scheduling Problems
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July 2023
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Publication History
Published: 24 July 2023
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- Luxembourg National Research Fund (FNR)
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GECCO '23 Companion
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GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation
July 15 - 19, 2023
Lisbon, Portugal
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