1. A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem.
- Author
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Sánchez-Díaz, Xavier, Ortiz-Bayliss, José Carlos, Amaya, Ivan, Cruz-Duarte, Jorge M., Conant-Pablos, Santiago Enrique, and Terashima-Marín, Hugo
- Subjects
KNAPSACK problems ,PROBLEM solving ,BACKPACKS ,ALGORITHMS ,HEURISTIC - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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