**Meet the editors**

Prof. Dr. Javier Del Ser received his first PhD degree in Telecommunication Engineering from the University of Navarra, Spain, and his second PhD degree in Computational Intelligence from the University of Alcala, Spain. He is a principal researcher in data analytics and optimization at Tecnalia, a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time

lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields. He has published more than 190 articles, co-supervised 6 PhD degree theses, edited 4 books, coauthored 6 patents and participated/led more than 35 research projects.

Dr. Eneko Osaba received his B.Sc. and M.Sc. degrees in Computer Science from the University of Deusto. He obtained his PhD degree in Artificial Intelligence in 2015 from the same university, being the recipient of a Basque Government doctoral grant. He has participated in the proposal, development and justification of 15 research projects. He has participated in the development of more

than 70 papers, having JCR impact factor for 20 of them. He has performed several stays in universities of the United Kingdom, Italy and Malta. He served as a member of the program and/or organizing committee in more than 15 international conferences, and he is a member of the editorial board of the International Journal of Artificial Intelligence.

Contents

**Preface VII**

Chapter 1 **Introductory Chapter: Nature-Inspired Methods for Stochastic,**

**Robust, and Dynamic Optimization 1**

Chapter 2 **Robust Optimization: Concepts and Applications 7**

Chapter 3 **Evaluation of Non-Parametric Selection Mechanisms in**

Chapter 4 **A Brief Survey on Intelligent Swarm-Based Algorithms for**

**Solving Optimization Problems 47** Siew Mooi Lim and Kuan Yew Leong

**Evolutionary Computation: A Case Study for the Machine**

Eneko Osaba and Javier Del Ser

José García and Alvaro Peña

**Scheduling Problem 23** Maxim A. Dulebenets

## Contents


**Chapter 1**

Provisional chapter

**Introductory Chapter: Nature-Inspired Methods for**

DOI: 10.5772/intechopen.78009

Optimization is one of the most studied fields in the wide field of artificial intelligence. Hundreds of studies published year after year focus on solving many diverse problems of this kind by resorting to a vast spectrum of solvers. Within this class of problems, several problem flavors can be identified depending on the characteristics of their constituent fitness functions and support of their optimization variables, such as linear, continuous or combinatorial. Efficiently tackling such optimization problems requires huge computational resources, especially when the formulated problem at hand represents complex real-world situations with hundreds of variables and constraints. For these reasons and due to the inherently practical utility of optimization algorithms, very heterogeneous problem-solving approaches have been developed by the community over the last decades for their application to these problems. From a general perspective, optimization methods can be classified as exact, heuristics, and metaheuristics. In this chapter, the focus is placed on the latter two families, in particular in those algorithmic variants where biological processes observed in nature have lied at the motivating core of the operators underlying their search mechanisms. In other words, we will center our

attention on Nature-Inspired methods for efficient optimization and problem solving.

In this context, Nature-Inspired algorithms have recently gained ever-growing popularity in the community, with an unprecedented body of the literature related to assorted algorithmic approaches suited to deal with problem formulations by leveraging the self-learning capability of their mimicked natural phenomena. The rationale behind the momentum acquired by this broad family of methods lies in their outstanding performance, which has hitherto been evinced in hundreds of research fields and problem scenarios. In this regard, many different inspirational sources have been proposed for constructing optimization methods, such as the behavioral patterns of bats [1], fireflies [1], bees [2] or the stigmergy by which ants communicate to each

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

and reproduction for non-commercial purposes, provided the original is properly cited.

© 2018 The Author(s). Licensee IntechOpen. Distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution

Introductory Chapter: Nature-Inspired Methods for

**Stochastic, Robust, and Dynamic Optimization**

Stochastic, Robust, and Dynamic Optimization

Eneko Osaba and Javier Del Ser

Eneko Osaba and Javier Del Ser

http://dx.doi.org/10.5772/intechopen.78009

1. Introduction

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter
