Using reinforcement learning for tuning genetic algorithms

Quevedo, José Ramón; Abdelatti, Marwan; Imani, Farhad; Sodhi, Manbir

doi:10.1145/3449726.3463203

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…RL provided new means for exploring and utilizing the search space, allowing the EAs to manage hyper-parameters. Crossover and mutation rates are the parameters that are regulated in GA; inertia weight and acceleration coefficient are controlled in PSO; and pheromone concentration and heuristic matrix are controlled by RL in ACO [168]. In order to solve TSP, Antonio Augusto Chaves and Luiz Henrique Nogueira Lorena (2021) proposed combining Q-Learning with The Biased Random-key genetic algorithm (BRKGA).…”

Section: -Parameter Settingmentioning

confidence: 99%

Learnheuristics in routing and scheduling problems: A review

Hussein¹,

Yaseen²,

Rashid³

2023

sjpas

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Combinatorial optimization problems (COPs) are the most important class of optimization problems, with great practical significance. This class is concerned with identifying the best solution from a discrete set of all available options. The transportation (routing) and distribution (scheduling) systems are considered the most challenging optimization examples of the COPs. Given the importance of routing and scheduling problems, many methods have been proposed to address them. These methods can be categorized into traditional (exact and metaheuristics (MHs) methods) and machine learning (ML) methods. ML methods have been proposed to overcome the problems that traditional methods suffer from, especially high computational time and dependence on the knowledge of experts. Recently, ML methods and MHs have been combined to tackle the COPs, and then the learnheuristics term emerged. This combination aims to guide the MHs toward an efficient, effective, and robust search and improve their performance in terms of solution quality. This work reviews the publications in which the collaboration between MHs and ML has been utilized to propose a guideline for the researchers to put forward new algorithms that have a good ability to solve routing and scheduling problems.

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Section: -Parameter Settingmentioning

confidence: 99%