A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem

Sánchez-Díaz, Xavier; Ortíz-Bayliss, José Carlos; Amaya, Iván; Cruz–Duarte, Jorge M.; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

doi:10.3390/app112110209

Cited by 8 publications

(2 citation statements)

References 59 publications

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“…The best hyper-heuristic constructed during the simulation can now be applied to unseen instances. The studies in [51,52] designed a feature-independent hyper-heuristic through an evolutionary algorithm to solve the 0/1 knapsack problem. Similar to the approach in the previous study [50], a set of training instances were used to construct viable hyperheuristics that were superior to the individual LLHs of the problem investigated.…”

Section: Related Studiesmentioning

confidence: 99%

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

2022

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Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms.

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Section: Related Studiesmentioning

confidence: 99%

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

2022

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“…As stated in Fig. 1, one may develop a seqBasedSelectionHH module that uses a HH model based on sequences of heuristics instead of rules [13,36], which was also mentioned in Section 1. We are actually working on such a kind of model, and we plan to provide it in a future release of MatHH.…”

Section: Software Architecturementioning

confidence: 99%