Data Clustering Using Grouping Hyper-heuristics

Elhag, Anas; Özcan, Ender

doi:10.1007/978-3-319-77449-7_7

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…The performances of various selection hyper-heuristics are compared using a set of benchmark instances which vary in terms of the number of items, groups as well as number ad nature of dimensions. The empirical results show that the proposed framework is indeed sufficiently general and reusable [34].…”

Section: A Review Of Heuristics Hyper-heuristics and Metaheuristics W...mentioning

confidence: 87%

A Heuristically Generated Metric Approach to the Solution of Chase Problem

Bucak¹

2022

Automation and Control - Theories and Applications

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In this work, heuristic, hyper-heuristic, and metaheuristic approaches are reviewed. Distance metrics are also examined to solve the “puzzle problems by searching” in AI. A viewpoint is brought by introducing the so-called Heuristically Generated Angular Metric Approach (HAMA) through the explanation of the metrics world. Distance metrics are applied to “cat and mouse” problem where cat and mouse makes smart moves relative to each other and therefore makes more appropriate decisions. The design is built around Fuzzy logic control to determine route finding between the pursuer and prey. As the puzzle size increases, the effect of HAMA can be distinguished more clearly in terms of computation time towards a solution. Hence, mouse will gain more time in perceiving the incoming danger, thus increasing the percentage of evading the danger. ‘Caught and escape percentages vs. number of cats’ for three distance metrics have been created and the results evaluated comparatively. Given three termination criteria, it is never inconsistent to define two different objective functions: either the cat travels the distance to catch the mouse, or the mouse increases the percentage of escape from the cat.

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Section: A Review Of Heuristics Hyper-heuristics and Metaheuristics W...mentioning

confidence: 87%

A Heuristically Generated Metric Approach to the Solution of Chase Problem

Bucak¹

2022

Automation and Control - Theories and Applications

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“… Kumari, Srinivas & Gupta (2013) proposed a fast multi-objective hyper-heuristic genetic algorithm MHypGA, which selects LLH based on adaptive weights that change as the search proceeds. Elhag & Özcan (2018) extended the grouped hyper-heuristic framework applied to graph coloring, using RL as a heuristic selection method to maintain a utility score for each LLH. Lamghari & Dimitrakopoulos (2020) combined RL and tabu search with HH and selected the heuristic based on the score of LLH and the tabu status.…”

Section: Value-based Reinforcement Learning Hyper-heuristicsmentioning

confidence: 99%

A review of reinforcement learning based hyper-heuristics

Li,

Wei,

Wang

et al. 2024

PeerJ Computer Science

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The reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the agent to dynamically adjust its own strategy, leading to a gradual optimization of the solution. Existing researches have shown the effectiveness of RL-HH in solving complex real-world problems. However, a comprehensive introduction and summary of the RL-HH field is still blank. This research reviews currently existing RL-HHs and presents a general framework for RL-HHs. This article categorizes the type of algorithms into two categories: value-based reinforcement learning hyper-heuristics and policy-based reinforcement learning hyper-heuristics. Typical algorithms in each category are summarized and described in detail. Finally, the shortcomings in existing researches on RL-HH and future research directions are discussed.

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“…Clustering is aggregating unlabeled objects into corporations with similarities between these objects. Such that the objects in the identical clusters are extra similar to every different object in distinct clusters in accordance to some predefined criteria [27] and [28]. A variety of algorithms have been proposed that take into account the nature of the data, the volume of the information and different enter parameters in order to cluster the data.…”

Section: Clustering Applicationsmentioning

confidence: 99%

Text Classification with the Whale Swarm Algorithm: A New Perspective

Marzouk,

F. Shawish,

Fathi

et al. 2023

Journal of Communication Sciences and Information Technology

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The increasing popularity of nature-inspired meta-heuristic algorithms in real-world optimization problems has been attributed to their advantages over traditional numerical optimization techniques. This study introduces the Whale Swarm Algorithm (WSA), a nature-inspired meta-heuristic, which draws inspiration from whales' communication through ultrasound for hunting. The focus of this paper is on using WSA for feature optimization. Text mining finds applications in various domains, such as business intelligence, social media analysis, sentiment analysis, biomedical analysis, software process analysis, and security analysis. This paper explores the use of WSA as an optimization algorithm, particularly in automating the understanding of Arabic text and constructing ontologies. Additionally, the paper delves into the challenges faced in enhancing the WSA models. Furthermore, this research presents a comprehensive overview of the diverse applications of WSA in different fields, with a specific emphasis on its role in ontology learning from Arabic text, aiming to improve swarm optimization techniques in practical use cases.

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Data Clustering Using Grouping Hyper-heuristics

Cited by 6 publications

References 22 publications

A Heuristically Generated Metric Approach to the Solution of Chase Problem

A Heuristically Generated Metric Approach to the Solution of Chase Problem

A review of reinforcement learning based hyper-heuristics

Text Classification with the Whale Swarm Algorithm: A New Perspective

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