Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems

• URL: http://arxiv.org/abs/2311.05937v2
• Date: Wed, 17 Jan 2024 10:52:46 GMT
• Title: Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems
• Authors: Maissa Irmouli, Nourelhouda Benazzoug, Alaa Dania Adimi, Fatma Zohra Rezkellah, Imane Hamzaoui, Thanina Hamitouche, Malika Bessedik, Fatima Si Tayeb
• Abstract summary: The proposed RL+GA method was specifically tested on the flow shop scheduling problem (FSP) The hybrid algorithm incorporates neural networks (NN) and uses the off-policy method Q-learning. Results of the study highlight the effectiveness of the RL+GA approach in improving the performance of the primitive GA.
• Score: 0.18846515534317265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a reinforcement learning (RL) approach to address the challenges associated with configuring and optimizing genetic algorithms (GAs) for solving difficult combinatorial or non-linear problems. The proposed RL+GA method was specifically tested on the flow shop scheduling problem (FSP). The hybrid algorithm incorporates neural networks (NN) and uses the off-policy method Q-learning or the on-policy method Sarsa(0) to control two key genetic algorithm (GA) operators: parent selection mechanism and mutation. At each generation, the RL agent's action is determining the selection method, the probability of the parent selection and the probability of the offspring mutation. This allows the RL agent to dynamically adjust the selection and mutation based on its learned policy. The results of the study highlight the effectiveness of the RL+GA approach in improving the performance of the primitive GA. They also demonstrate its ability to learn and adapt from population diversity and solution improvements over time. This adaptability leads to improved scheduling solutions compared to static parameter configurations while maintaining population diversity throughout the evolutionary process.

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