A Quartile-Based Hyper-heuristic for Solving the 0/1 Knapsack Problem

Gómez-Herrera, Fernando; Ramírez-Valenzuela, Rodolfo A.; Ortíz-Bayliss, José Carlos; Amaya, Iván; Terashima-Marín, Hugo

doi:10.1007/978-3-030-02837-4_10

Cited by 3 publications

(1 citation statement)

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“…Falkenauer [11] proposed a GA-based HH model, and Burke et al [17], Hyde [8], and Drake et al [9,18,19] have studied GP rules for the Bin Packing and Multidimensional Knapsack problems. More recent studies of HHs have been conducted on the binary knapsack domain using Ant Colony Optimization [20] and Quartile-related Information [21]. Further information about the state-of-the-art of HHs and their applications are provided in [4,22].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform single heuristics when evaluated in isolation. When hyper-heuristics are selected among existing algorithms, they map problem states into suitable solvers. Unfortunately, identifying the features that accurately describe the problem state—and thus allow for a proper mapping—requires plenty of domain-specific knowledge, which is not always available. This work proposes a simple yet effective hyper-heuristic model that does not rely on problem features to produce such a mapping. The model defines a fixed sequence of heuristics that improves the solving process of knapsack problems. This research comprises an analysis of feature-independent hyper-heuristic performance under different learning conditions and different problem sets.

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