Adaptation and Hybridization in Computational Intelligence

  • Iztok Fister
  • Iztok Fister Jr.

Part of the Adaptation, Learning, and Optimization book series (ALO, volume 18)

Table of contents

  1. Front Matter
    Pages 1-10
  2. Background Information and Theoretical Foundations of Computational Intelligence

    1. Front Matter
      Pages 1-1
    2. Iztok Fister, Damjan Strnad, Xin-She Yang, Iztok Fister Jr.
      Pages 3-50
  3. Adaptation in Computational Intelligence

    1. Front Matter
      Pages 51-51
    2. Janez Brest, Aleš Zamuda, Borko Bošković
      Pages 53-68
    3. Iztok Fister Jr., Iztok Fister
      Pages 69-89
    4. Gai-Ge Wang, Amir H. Gandomi, Amir H. Alavi
      Pages 111-128
    5. Seyyed Soheil Sadat Hosseini, Xin-She Yang, Amir H. Gandomi, Alireza Nemati
      Pages 129-146
  4. Hybridization in Computational Intelligence

    1. Front Matter
      Pages 147-147
    2. Tomaž Hozjan, Goran Turk, Iztok Fister
      Pages 149-169
    3. M. Fatih Tasgetiren, P. N. Suganthan, Sel Ozcan, Damla Kizilay
      Pages 171-184
    4. Yannis Marinakis, Magdalene Marinaki, Paraskevi Spanou
      Pages 185-204
    5. Boonserm Kaewkamnerdpong, Pinfa Boonrong, Supatchaya Trihirun, Tiranee Achalakul
      Pages 205-236
  5. Back Matter
    Pages 237-237

About this book

Introduction

 

This carefully edited book takes a walk through recent advances in adaptation and

hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that

are divided into three parts. The first part illustrates background information and provides

some theoretical foundation tackling the CI domain, the second part

deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI.

This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization,

modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.

 

Keywords

Adaptation Computational Intelligence Cuckoo Search Data Mining Firefly Algorithm Natural Selection Optimization Particle Swarm Optimization Self-adaptation Tuning

Editors and affiliations

  • Iztok Fister
    • 1
  • Iztok Fister Jr.
    • 2
  1. 1.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia
  2. 2.Faculty of Electrical Engineering and Computer ScienceUniversity of MariborMariborSlovenia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-14400-9
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-14399-6
  • Online ISBN 978-3-319-14400-9
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • About this book