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Variants of Evolutionary Algorithms for Real-World Applications

  • Raymond Chiong
  • Thomas Weise
  • Zbigniew Michalewicz

Table of contents

  1. Front Matter
  2. Section I: Introduction

    1. Christian Blum, Raymond Chiong, Maurice Clerc, Kenneth De Jong, Zbigniew Michalewicz, Ferrante Neri et al.
      Pages 1-29
  3. Section II: Planning and Scheduling

    1. Arvind Mohais, Sven Schellenberg, Maksud Ibrahimov, Neal Wagner, Zbigniew Michalewicz
      Pages 31-58
    2. Claudio F. M. Toledo, Marcio S. Arantes, Paulo M. França, Reinaldo Morabito
      Pages 59-93
    3. Jörg Lässig, Christian A. Hochmuth, Stefanie Thiem
      Pages 95-141
    4. Sven Schellenberg, Arvind Mohais, Maksud Ibrahimov, Neal Wagner, Zbigniew Michalewicz
      Pages 143-166
    5. Dipankar Dasgupta, Deon Garrett, Fernando Nino, Alex Banceanu, David Becerra
      Pages 167-203
    6. Guohua Ma, Fu Zhang
      Pages 205-244
  4. Section III: Engineering

    1. Mohsen Davarynejad, Jos Vrancken, Jan van den Berg, Carlos A. Coello Coello
      Pages 245-280
  5. Section IV: Data Collection, Retrieval and Mining

    1. Amir Hossein Alavi, Amir Hossein Gandomi, Ali Mollahasani
      Pages 343-376
    2. Eleonora Bilotta, Antonio Cerasa, Pietro Pantano, Aldo Quattrone, Andrea Staino, Francesca Stramandinoli
      Pages 377-412
    3. Marlene Goncalves, Ivette Martínez, Gabi Escuela, Fabiola Di Bartolo, Francelice Sardá
      Pages 413-436
    4. Cem Şafak Şahin, Elkin Urrea, M. Ümit Uyar, Stephen Gundry
      Pages 437-462
  6. Back Matter

About this book

Introduction

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book “Variants of Evolutionary Algorithms for Real-World Applications” aims to promote the practitioner’s view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the prediction of soil properties, automated tissue classification for MRI images, and database query optimisation, among others. These chapters demonstrate how different types of problems can be successfully solved using variants of EAs and how the solution approaches are constructed, in a way that can be understood and reproduced with little prior knowledge on optimisation.

Keywords

Differential Evolution Evolution Strategies Evolutionary Programming Genetic Algorithms Genetic Programming

Editors and affiliations

  • Raymond Chiong
    • 1
  • Thomas Weise
    • 2
  • Zbigniew Michalewicz
    • 3
  1. 1.Faculty of ICTSwinburne University of TechnologyMelbourneAustralia
  2. 2.Nature Inspired Computation and Applications Laboratory School of Computer Science and TechnologyUniversity of Science and Technology of China (USTC)HefeiChina
  3. 3.School of Computer ScienceUniversity of AdelaideAdelaideAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-23424-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-23423-1
  • Online ISBN 978-3-642-23424-8
  • Buy this book on publisher's site