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Metaheuristics for machine learningnew advances and tools /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Metaheuristics for machine learningedited by Mansour Eddaly, Bassem Jarboui, Patrick Siarry.
Reminder of title:	new advances and tools /
other author:	Eddaly, Mansour.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xv, 223 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Metaheuristics.
Online resource:	https://doi.org/10.1007/978-981-19-3888-7
ISBN:	9789811938887$q(electronic bk.)

Metaheuristics for machine learningnew advances and tools /
Metaheuristics for machine learningnew advances and tools /[electronic resource] :edited by Mansour Eddaly, Bassem Jarboui, Patrick Siarry. - Singapore :Springer Nature Singapore :2023. - xv, 223 p. :ill., digital ;24 cm. - Computational intelligence methods and applications,2510-1773. - Computational intelligence methods and applications..

1. From metaheuristics to automatic programming -- 2. Biclustering Algorithms Based on Metaheuristics: A Review -- 3. A Metaheuristic Perspective on Learning Classifier Systems -- 4. An evolutionary clustering approach using metaheuristics and unsupervised machine learning algorithms for customer segmentation -- 5. Applications of Metaheuristics in Parameter Optimization in Manufacturing Processes and Machine Health Monitoring -- 6. Evolving Machine Learning-based classifiers by metaheuristic approaches for underwater sonar target detection and recognition -- 7. Solving the Quadratic Knapsack Problem using a GRASP algorithm based on a multi-swap local search -- 8. Algorithmic vs Processing Manipulations to Scale Genetic Programming to Big Data Mining -- 9. Dynamic assignment problem of parking slots.

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.

ISBN: 9789811938887$q(electronic bk.)

Standard No.: 10.1007/978-981-19-3888-7doiSubjects--Topical Terms:

757062
Metaheuristics.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.13

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505 0 $a 1. From metaheuristics to automatic programming -- 2. Biclustering Algorithms Based on Metaheuristics: A Review -- 3. A Metaheuristic Perspective on Learning Classifier Systems -- 4. An evolutionary clustering approach using metaheuristics and unsupervised machine learning algorithms for customer segmentation -- 5. Applications of Metaheuristics in Parameter Optimization in Manufacturing Processes and Machine Health Monitoring -- 6. Evolving Machine Learning-based classifiers by metaheuristic approaches for underwater sonar target detection and recognition -- 7. Solving the Quadratic Knapsack Problem using a GRASP algorithm based on a multi-swap local search -- 8. Algorithmic vs Processing Manipulations to Scale Genetic Programming to Big Data Mining -- 9. Dynamic assignment problem of parking slots.
520 $a Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.
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