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Evolutionary Computation Techniques: A Comparative Perspective

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  • © 2017

Overview

Authors:
  1. Erik Cuevas

    1. Universidad de Guadalajara, CUCEI, Guadalajara, Mexico

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  2. Valentín Osuna

    1. Universidad de Guadalajara, CUTONALA Universidad de Guadalajara, Tonalá Jalisco, Mexico

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  3. Diego Oliva

    1. Universidad de Guadalajara, CUCEI, Guadalajara, Mexico

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  • Compares various evolutionary computation (EC) techniques when they face complex optimization problems extracted from different engineering domains
  • Poses a complex engineering optimization problem in each self-contained chapter
  • Presents specific EC techniques as the best choice according to their search characteristics and conducts a set of experiments to compare their performance to other popular EC methods
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 686)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Introduction

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 1-8

  3. Multilevel Segmentation in Digital Images

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 9-33

  4. Multi-circle Detection on Images

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 35-64

  5. Template Matching

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 65-93

  6. Motion Estimation

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 95-116

  7. Photovoltaic Cell Design

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 117-138

  8. Parameter Identification of Induction Motors

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 139-154

  9. White Blood Cells Detection in Images

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 155-180

  10. Estimation of View Transformations in Images

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 181-204

  11. Filter Design

    • Erik Cuevas, Valentín Osuna, Diego Oliva
    Pages 205-222
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Keywords

About this book

This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.

Authors and Affiliations

  • Universidad de Guadalajara, CUCEI, Guadalajara, Mexico

    Erik Cuevas, Diego Oliva

  • Universidad de Guadalajara, CUTONALA Universidad de Guadalajara, Tonalá Jalisco, Mexico

    Valentín Osuna

Bibliographic Information

  • Book Title: Evolutionary Computation Techniques: A Comparative Perspective

  • Authors: Erik Cuevas, Valentín Osuna, Diego Oliva

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-51109-2

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2017

  • Hardcover ISBN: 978-3-319-51108-5Published: 07 January 2017

  • Softcover ISBN: 978-3-319-84568-5Published: 07 July 2018

  • eBook ISBN: 978-3-319-51109-2Published: 28 December 2016

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XV, 222

  • Number of Illustrations: 41 b/w illustrations, 33 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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