A Fuzzy Hyper-Heuristic Approach for the 0-1 Knapsack Problem

Olivas, Frumen; Amaya, Iván; Ortíz-Bayliss, José Carlos; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

doi:10.1109/cec48606.2020.9185710

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Another interesting application of hyper-heuristics is in fuzzy logic systems. In the model [44], a GA is responsible for finding the fuzzy rule set that maximizes the fuzzy inference system's performance. The GA used in this work is a custom discrete version of the original GA, where the genes can only have integer values, i.e., 1, 2, 3, or 4, representing the heuristic selected by the fuzzy rule.…”

Section: ) Hyper-heuristicsmentioning

confidence: 99%

Problem Solving in Crowd Management Using Heuristic Approach

et al. 2022

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There are many problems that procedural algorithms can solve efficiently. However, these algorithms are sometimes too slow to abide by the time available for performing the solution; other times, it is impossible to get a solution using procedural algorithms. A heuristic method is a practical approach that can reach an approximation of an efficient solution where the optimum is not guaranteed. Heuristic techniques are applied in many real-world problems, including crowd management; using heuristic-based models helped to comprehend crowd behavior better and increase simulation reliability. This paper reviews many heuristic-related articles to gather the aspects of the topic in one place and clear the fuzziness to make it easy to comprehend. The paper covers some of the previous works with similar approaches and presents state-of-the-art heuristic solutions for real-world problems. These techniques are discussed under three classifications: simple heuristics, meta-heuristics, and hyper-heuristics. Most importantly, the paper explores the heuristic role in crowd field problems concluding that heuristics are primarily applied in modeling when it comes to the Crowd field. It investigates different heuristics for crowd management. The main intent of this review is to establish a comprehensive understanding of heuristics-related operations in the crowd management field. Moreover, it aims to support other researchers' future work and fill research gaps by highlighting the absence of crowd problems from heuristics literature and the limitations of each heuristics approach.

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Section: ) Hyper-heuristicsmentioning

confidence: 99%

Problem Solving in Crowd Management Using Heuristic Approach

et al. 2022

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“…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. Olivas et al [15,53] incorporated fuzzy logic into the inference process of the selection of heuristics for the knapsack problem. The authors considered seven features of the problem as inputs to the fuzzy inference engine and four LLHs as outputs.…”

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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An Exploratory Study on Machine-Learning-Based Hyper-heuristics for the Knapsack Problem

Zárate-Aranda,

Ortiz-Bayliss

2024

Lecture Notes in Computer Science

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A Fuzzy Hyper-Heuristic Approach for the 0-1 Knapsack Problem

Cited by 4 publications

References 22 publications

Problem Solving in Crowd Management Using Heuristic Approach

Problem Solving in Crowd Management Using Heuristic Approach

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

An Exploratory Study on Machine-Learning-Based Hyper-heuristics for the Knapsack Problem

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