Call for book Chapters Springer MAML

Introduction

Optimization is present if many fields of science and technology because it is always necessary to find the best value in different implementations.  In recent years the use of metaheuristic algorithms (MA) have been increasing due to its flexibility and easy implementation. MA are optimization tools designed to work with difficult problems where a set of solutions is created to explore the search space. Usually, MA can behave in two forms; exploration, and exploitation. The exploration phase is used to diversify the solutions along the search space to avoid local stagnation, while the exploitation phase intensifies the search in a region to ensure convergence.

On the other hand, machine learning (ML) is a field that has also attracted the attention in different fields of application. ML techniques are used to analyse and classify different amounts of information. For that reason, they are very popular. However, depending on the algorithm used they exist different drawbacks, for example during the training processes. Different issues in ML can be addressed as optimization problems and it is a tendency to use MA to help the ML approaches.

The book is focused on the theory and application of MA in machine learning, including hybridization and implementations in different fields. In this sense, topics are selected based on their importance and complexity in this field — for example, biochemistry, image processing, clustering, feature selection, energy, among others.

We invite all researchers and practitioners who are developing algorithms, systems, and applications, to share their results, ideas, and experiences.

Topics of interest include, but are not limited to, the following: