Preface

The solution to many real-world problems lies in optimizing processes, parameters, or techniques. However, these optimizations usually mean dealing with immense search spaces and thus require exhaustive methods to evaluate all possible solutions in the search for a global optimum. In addition, many local optima may exist in the search space, so simple techniques may get stuck in them. Evolutionary algorithms and more concrete genetic algorithms are metaheuristic techniques inspired by Darwin's theory of natural selection to solve search-based optimization problems. These algorithms have been demonstrated to effectively deal with complex search spaces. Genetic algorithms employ a population of individuals, each representing a full or partial solution to the problem, bred and reproduced looking for optimal individuals. These individuals are evaluated according to a fitness function, which determines how a given individual adapts to the problem at hand.

In recent years, genetic algorithms have advanced by proposing novel algorithmic flows, representations, or specific techniques inside the main structure of the algorithm. As a result, genetic algorithms have been successfully applied to solve many real-world problems (engineering, smart cities, and energy). They have also helped to improve many machine learning (classification, regression, or hyperparameter optimization) and data mining (data preprocessing, pattern mining, or feature selection) techniques.

This book provides a comprehensive overview of the current state of the art and advances in genetic algorithms and examines the fields in which they have been applied throughout the years. It is divided into several sections, including an introduction to genetic algorithms and a summary of their applications in numerous scientific fields. An additional section includes chapters related to engineering applications, covering fields such as power systems signal stability, design of digital circuits, chemical optimization, and controller systems. This book also describes the use of genetic algorithms in architecture.

> **Sebastián Ventura, José María Luna and José María Moyano** University of Cordoba, Cordoba, Spain

Section 1

Introduction

**1**

Section 1 Introduction

#### **Chapter 1**
