Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Calvet, Laura; Armas, Jésica de; Masip, David; Juan, Ángel A.

doi:10.1515/math-2017-0029

Cited by 139 publications

(75 citation statements)

References 128 publications

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“…Audience members suggested that one direction of future work might be to integrate heuristics with machine learning and other artificial intelligence methods. Through the information gathered and generated by these computational methods, assistance may be provided to a heuristic search process through the dynamic tuning of parameters and through a reduction in the solution space searched [40]. Efforts along these lines may be of benefit to the suggestions we posed for future research in Section 2.4.…”

Section: Discussionmentioning

confidence: 99%

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Bettinger

Boston

2017

Forests

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Adaptive forest management requires planning and implementation of activities designed to maintain or improve forest conditions, and in support of these endeavors knowledge of silviculture, economics, operations research, and other allied fields are necessary. With regard to forest planning, traditional (exact) mathematical techniques along with heuristics have been demonstrated as useful in developing alternative courses of action for forest managers to consider. In this discussion paper, we present six areas of future work with regard to investigations into the development of heuristics, along with several recommendations that are based on our experiences. These areas include process improvements, reversion strategies, destruction and reconstruction strategies, intelligent or dynamic parameterization approaches, intelligent termination or transitioning approaches, and seeding strategies. We chose the six areas based on our experiences in developing forest planning heuristics. These areas reflect our opinion of where future research might concentrate. All of these areas of work have the potential to enhance the capabilities and effectiveness of heuristic approaches when applied to adaptive forest management problems.

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Section: Discussionmentioning

confidence: 99%

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Bettinger

Boston

2017

Forests

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“…Then, the extracted knowledge, which is usually represented by a model or rules, is used to tune or substitute for a component of an optimisation algorithm. Multiple overviews have recently covered the interactions between ML and metaheuristics [33,39,84,197], however, to the best of our knowledge, there is no survey presenting a categorisation on the way that ML is used for enhancing hyper-heuristics. In addition, ML techniques can also be used to choose the best performing algorithm for a particular optimisation problem.…”

Section: Machine Learning For Optimisationmentioning

confidence: 99%

“…To tackle these problems, ML techniques have been used to accelerate the search process and improve the quality of solutions. There are several previous review works about improving metaheuristics by ML [33,39,84,197]. They provided similar taxonomies and one of them proposed in [84] depended on three different criteria, namely localisation, aim and kind of knowledge.…”

Section: Machine Learning For Metaheuristic Optimisationmentioning

confidence: 99%

“…In the past few years, a number of review papers have been published about the dual interactions [18,33,39], individual interactions [5,24,84,97,177] and the interactions that focused on a specific ML or optimisation algorithm [16,56,58,79,117,118,125,189,193,197] between ML and optimisation. Differently, our study provides a global overview of the interaction between ML and optimisation as well as those within themselves as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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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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“…These types of modifications correspond to small modifications of the algorithm since the operating mechanism of the algorithm is not altered. However, many optimization problems cannot be efficiently addressed by an algorithm through modification of its parameters and require deep modifications that alter its mechanism of operation [17,26]. A strategy that has strengthened the results of metaheuristic algorithms has been the hybridization of these with techniques that come from the same and the other areas.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Cited by 139 publications

References 128 publications

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

Forest Planning Heuristics—Current Recommendations and Research Opportunities for s-Metaheuristics

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

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

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