Metaheuristics for Machine Learning: New Advances and Tools

ISBN-13: 9789811938870 / Angielski / Twarda / 2023 / 246 str.

Mansour Eddaly; Bassem Jarboui; Patrick Siarry

Opis

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.

Spis treści

From metaheuristics to automatic programming

S. Elleuch, B. Jarboui and P. Siarry

Biclustering Algorithms Based on Metaheuristics: A Review

A. José-García,  J. Jacques, V. Sobanski, C. Dhaenens

A Metaheuristic Perspective on Learning Classifier Systems

M. Heider, D. Pätzel, H. Stegherr and J. Hähner

An evolutionary clustering approach using metaheuristics and unsupervised machine learning algorithms for customer segmentation

P.Z. Lappas, A.N. Yannacopoulos and S. Xanthopoulos

Applications of Metaheuristics in Parameter Optimization in Manufacturing Processes and Machine Health Monitoring

A. Banerjee, I. Abu-Mahfouz and A.H.M Esfakur Rahman

Evolving Machine Learning-based classifiers by metaheuristic approaches for underwater sonar target detection and recognition

M. Khishe, H. Javdanfar, M. Kazemirad, H. Mohammadi

Solving the Quadratic Knapsack Problem using a GRASP algorithm based on a multi-swap local search

R. Jovanovic and S. Voss

Algorithmic vs Processing Manipulations to Scale Genetic Programming to Big Data Mining}

S. Ben Hamida and H. Hmida

Dynamic assignment problem of parking slots

Mustapha Ratli, Abdessamad Ait El Cadi and Bassem Jarboui

O autorze

Mansour Eddaly is an assistant professor in the College of Business and Economics at Qassim University (KSA). His current research interests mainly involve combinatorial optimization, metaheuristics, and computational intelligence.

Bassem Jarboui is Full Professor of Operational Research at Sfax University, Tunisia, where he also completed his PhD. Currently, he is working at the Higher Colleges of Technology, Abu Dhabi, UAE. He has edited seven books and two special journal issues. He has also organized and chaired five international conferences. He has published over 130 scientific papers, including articles, contributions to edited proceedings, and book chapters.

Patrick Siarry received his PhD from the University of Paris 6 in 1986 and his Doctor of Sciences (Habilitation) from the University of Paris 11 in 1994. He first became involved in the development of analogue and digital models of nuclear power plants at Électricité de France (E.D.F.). He has been Professor of Automatics and Informatics since 1995. His main research interest is in the applications of new stochastic global optimization heuristics to various engineering fields.