Introduction to Evolutionary Algorithms

  • Xinjie Yu
  • Mitsuo Gen

Part of the Decision Engineering book series (DECENGIN, volume 0)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Evolutionary Algorithms

    1. Front Matter
      Pages 1-1
    2. Pages 3-10
  3. Dealing with Complicated Problems

    1. Front Matter
      Pages 133-133
    2. Pages 135-164
    3. Pages 165-191
  4. Brief Introduction to Other Evolutionary Algorithms

    1. Front Matter
      Pages 325-325
    2. Pages 327-354
    3. Pages 381-401
  5. Back Matter
    Pages 403-418

About this book


Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.


algorithms artificial life combinatorial optimization evolution evolutionary algorithm genetic algorithm genetic programming kernel learning multi-objective optimization operations research optimization swarm intelligence

Authors and affiliations

  • Xinjie Yu
    • 1
  • Mitsuo Gen
    • 2
  1. 1.Department of Electrical EngineeringTsinghua UniversityBeijingChina
  2. 2.Fuzzy Logic Systems Institute (FLSI)IizukaJapan

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London 2010
  • Publisher Name Springer, London
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-1-84996-128-8
  • Online ISBN 978-1-84996-129-5
  • Series Print ISSN 1619-5736
  • Buy this book on publisher's site