Automatic design of hyper-heuristic based on reinforcement learning

Choong, Shin Siang; Wong, Li-Pei; Lim, Chee Peng

doi:10.1016/j.ins.2018.01.005

Cited by 92 publications

(35 citation statements)

References 23 publications

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“…In [70], RL was used to choose one of the lower heuristics from 30 actions, with three non-overlapping ranges as the number of states. In its process, the given computational time is divided into a number of equal epochs (|E|), which was determined by applying the algorithm on the instances taken from CHeSC 2011.…”

Section: Rl In Easmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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Section: Rl In Easmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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“…Two approaches in [95,121] used such simple RL scheme to reward and punish the weights of low-level heuristics based on their previous performance and selected low-level heuristic using roulette wheel method according to their weights in each iteration. Rather than using such simple RL scheme, a more complex RL method, Q-learning, was introduced to learn to select heuristics in [36], which strictly follows the criteria of RL. In [47], the author proposed a RL-based hyper-heuristic method that fulfilled the criteria of RL.…”

Section: Rl-based Approachesmentioning

confidence: 99%

A review on the self and dual interactions between machine learning and optimisation

2019

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Machine learning and optimisation are two growing fields of artificial intelligence with an enormous number of computer science applications. The techniques in the former area aim to learn knowledge from data or experience, while the techniques from the latter search for the best option or solution to a given problem. To employ these techniques automatically and effectively aligning with the real aim of artificial intelligence, both sets of techniques are frequently hybridised, interacting with each other and themselves. This study focuses on such interactions aiming at (1) presenting a broad overview of the studies on self and dual interactions between machine learning and optimisation; (2) providing a useful tutorial for researchers and practitioners in both fields in support of collaborative work through investigation of the recent advances and analyses of the advantages and disadvantages of different techniques to tackle the same or similar problems; (3) clarifying the overlapping terminologies having different meanings used in both fields; (4) identifying research gaps and potential research directions.

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“…In [29], Ahmed and Ender proposed an iterated multi-stage selection hyperheuristic and investigated on one-dimensional bin-packing, personal scheduling, permutation flow-shop, travelling salesman problem, and vehicle routing problem. In [12], a reinforcement learning technique is incorporated in hyper-heuristic and the performance is evaluated on six different problem domains. Several state-of-the-art hyper-heuristics [6,12] are also developed…”

Section: Overview Of Hyper-heuristicmentioning

confidence: 99%

An evolutionary algorithm based hyper-heuristic framework for the set packing problem

Chaurasia

Kim

2019

Information Sciences

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights• An evolutionary algorithm based hyper-heuristic is proposed for the set packing problem (SPP) and the minimum weight dominating set (MWDS) problem.• Self-learning concept is employed in hyper-heuristic for a selection of heuristic.• Dynamic selection of parameter is adopted to make the approach, as much as possible, less dependent on parameter values. The empirical study shows the effectiveness of Dynamic selection of parameter.• The proposed approach has been compared with the respective state-of-the-art approaches for both the SPP and MWDS problem.• Computational results show the superiority of the proposed approach.

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Automatic design of hyper-heuristic based on reinforcement learning

Cited by 92 publications

References 23 publications

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

A review on the self and dual interactions between machine learning and optimisation

An evolutionary algorithm based hyper-heuristic framework for the set packing problem

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