Metaheuristics for Machine Learning

1. Front Matter

   Pages i-xv

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2. Metaheuristics for Machine Learning: Theory and Reviews

   1. Front Matter

      Pages 1-1

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   2. From Metaheuristics to Automatic Programming

      • S. Elleuch, B. Jarboui, P. Siarry

      Pages 3-38

   3. Biclustering Algorithms Based on Metaheuristics: A Review

      • Adán José-García, Julie Jacques, Vincent Sobanski, Clarisse Dhaenens

      Pages 39-71

   4. A Metaheuristic Perspective on Learning Classifier Systems

      • Michael Heider, David Pätzel, Helena Stegherr, Jörg Hähner

      Pages 73-98

3. Metaheuristics for Machine Learning: Applications

   1. Front Matter

      Pages 99-99

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   2. Metaheuristic-Based Machine Learning Approach for Customer Segmentation

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

      Pages 101-133

   3. Evolving Machine Learning-Based Classifiers by Metaheuristic Approach for Underwater Sonar Target Detection and Recognition

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

      Pages 135-155

   4. Solving the Quadratic Knapsack Problem Using GRASP

      • Raka Jovanovic, Stefan Voß

      Pages 157-178

   5. Algorithm vs Processing Manipulation to Scale Genetic Programming to Big Data Mining

      • S. Ben Hamida, H. Hmida

      Pages 179-199

   6. Dynamic Assignment Problem of Parking Slots

      • M. Ratli, A. Ait El Cadi, B. Jarboui, M. Eddaly

      Pages 201-223

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.

• Machine learning
• Metaheuristics
• Artificial intelligence
• Automatic programming
• Evolutionary computation
• Swarm intelligence

• Qassim University, Buraydah, Saudi Arabia

  Mansour Eddaly

• Abu Dhabi Women Campus, Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

  Bassem Jarboui

• Paris-Est Créteil University, Paris, France

  Patrick Siarry

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.

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