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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.

<p><b>Mansour Eddaly</b>&nbsp;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.</p>

<p><b>Bassem Jarboui</b>&nbsp;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.</p>

<p><b>Patrick Siarry</b>&nbsp;received his PhD from the University of Paris 6 in 1986 and his Doctor of Sciences (<i>Habilitation</i>) 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.</p><br>

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.<p></p>

Presents latest developments in integrating metaheuristics into machine learning techniques Illustrates practical applications of metaheuristics in machine learning Offers an overview of main metaheuristic programming methods