Diego Oliva - Metaheuristics in Machine Learning: Theory and Applications

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In recent years, metaheuristics (MHs) have become important tools for solving hard optimization problems encountered in industry, engineering, biomedical, image processing, as well as in the theoretical field. Several different metaheuristics exist, and new ones are under constant development. One of the most fundamental principles in our world is the search for an optimal state. Therefore, choosing the right solution method for an optimization problem can be crucially important in finding the right solutions for a given optimization problem (unconstrained and constrained optimization problems). There exist a diverse range of MHs for optimization. Optimization is an important and decisive activity in science and engineering. Engineers will be able to produce better designs when they can save time and decrease the problem complexity with optimization methods. Many engineering optimization problems are naturally more complex and difficult to solve by conventional optimization methods such as dynamic programming. In recent years, more attention has been paid to innovative methods derived from the nature that is inspired by the social or the natural systems, which have yielded outstanding results in solving complex optimization problems. Metaheuristic algorithms are a type of random algorithm which is used to find the optimal solutions. Metaheuristics are approximate types of optimization algorithms that can better escape from the local optimum points and can be used in a wide range of engineering problems.

Recently, metaheuristics (MHs) and Machine learning (ML) became a very important and hot topic to solve real-world applications in the industrial world, science, engineering, etc. Among the subjects to be considered are theoretical developments in MHs; performance comparisons of MHs; cooperative methods combining different types of approaches such as constraint programming and mathematical programming techniques; parallel and distributed MHs for multi-objective optimization; adaptation of discrete MHs to continuous optimization; dynamic optimization; software implementations; and real-life applications. Besides, Machine learning (ML) is a data analytics technique to use computational methods. Therefore, recently, MHs have been combined with several ML techniques to deal with different global and engineering optimization problems, also real-world applications. Chapters published in the Metaheuristics in machine learning: theory and applications (MAML2020) book describe original works in different topics in both science and engineering, such as: Metaheuristics, Machine learning, Soft Computing, Neural Networks, Multi-criteria decision making, energy efficiency, sustainable development, etc.

In short, it can be said that metaheuristic algorithms and machine learning are advanced and general search strategies. Therefore, the main contribution of this book is to indicate the advantages and importance of metaheuristics with machine learning in various real-world applications.

This book MAML2020 collects several hybridized metaheuristics (MHs) with machine learning (ML) methods for various real-world applications. Hence, the MHs have become essential tools for solving hard optimization problems encountered in industry, engineering, biomedical, image processing, as well as in the theoretical field. Besides, machine learning (ML) is a data analytics technique to use computational methods. Therefore, recently, MHs have been combined with several ML techniques to deal with different global and engineering optimization problems, also real-world applications. However, this book addresses the issues of two important computer sciences strategies: MHs and ML. The idea of combining the techniques is to improve the performance of the original methods in different applications.