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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.
Metaheuristics are well known as an efficient tool to solve hard optimization problems. The idea behind is to maintain the balance between diversification and intensification to provide high-quality solutions in a reasonable time. Research and development in employing metaheuristics to enhance Machine Learning techniques become trendy. Some successful applications are used for both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, the automatic generation of programs through metaheuristics, such as evolutionary computation and swarm intelligence, has gained a significant widespread popularity. This success has been mostly promoted by the genetic programming paradigm developed by Koza in 1992.
In this book, we investigate different ways for using metaheuristics into machine learning techniques from both theoretical and practical points of view. This book reviews the latest innovations and applications of integrating metaheuristics into machine learning techniques, covering metaheuristic programming, meta-learning, etc. Moreover, some illustrations through real-world applications are given, including clustering, big data, machine health monitoring, underwater sonar targets, banking, etc.
This book is divided into two main parts: the first part ”Metaheuristics for machine learning: theory and reviews” includes three chapters and deals with theoretical aspects, whereas the second part ”Metaheuristics for machine learning: applications” includes five chapters and discusses some real-world applications.
Part I. Metaheuristics for Machine Learning: Theory and Reviews
1. From Metaheuristics to Automatic Programming
2. Biclustering Algorithms Based on Metaheuristics: A Review
3. A Metaheuristic Perspective on Learning Classifier Systems
Part II. Metaheuristics for Machine Learning: Applications
4. Metaheuristic-Based Machine Learning Approach for Customer Segmentation
5. Evolving Machine Learning-Based Classifiers by Metaheuristic Approach for Underwater Sonar Target Detection and Recognition
6. Solving the Quadratic Knapsack Problem Using GRASP
7. Algorithm vs Processing Manipulation to Scale Genetic Programming to Big Data Mining
8. Dynamic Assignment Problem of Parking Slots