Table of contents

  1. Front Matter
    Pages 1-12
  2. Introduction

    1. Front Matter
      Pages 1-1
  3. Part I Introduction

    1. Enrique Alba, Bernabè Dorronsoro
      Pages 3-20
    2. Enrique Alba, Bernabè Dorronsoro
      Pages 21-34
  4. Characterizing Cellular Genetic Algorithms

    1. Front Matter
      Pages 1-1
  5. Part II Characterizing Cellular Genetic Algorithms

    1. Enrique Alba, Bernabè Dorronsoro
      Pages 37-46
    2. Enrique Alba, Bernabè Dorronsoro
      Pages 47-69
  6. Algorithmic Models and Extensions

    1. Front Matter
      Pages 1-1
  7. Part III Algorithmic Models and Extensions

    1. Enrique Alba, Bernabè Dorronsoro
      Pages 73-82
    2. Enrique Alba, Bernabè Dorronsoro
      Pages 83-99
    3. Enrique Alba, Bernabè Dorronsoro
      Pages 101-114
    4. Enrique Alba, Bernabè Dorronsoro
      Pages 115-126
    5. Enrique Alba, Bernabè Dorronsoro
      Pages 127-138
    6. Enrique Alba, Bernabè Dorronsoro
      Pages 139-152
    7. Enrique Alba, Bernabè Dorronsoro
      Pages 153-163
  8. Applications of cGAs

    1. Front Matter
      Pages 1-1
  9. Part IV Applications of cGAs

    1. Enrique Alba, Bernabè Dorronsoro
      Pages 167-174
    2. Enrique Alba, Bernabè Dorronsoro
      Pages 175-186
    3. Enrique Alba, Bernabè Dorronsoro
      Pages 187-202
    4. Enrique Alba, Bernabè Dorronsoro
      Pages 203-210

About this book

Introduction

CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability.

The methods are benchmarked against well-known metaheutistics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.

Keywords

Optimization algorithm Optimization algorithms algorithm algorithms bioinformatics communication evolutionary algorithm genetic algorithms metaheuristic multi-objective optimization optimization

Authors and affiliations

  • Bernabe Dorronsoro
  • Enrique Alba
  1. 1.Escuela Técnica Superior de IngenieríaUniversidad MálagaMálagaSpain
  2. 2.Escuela Técnica Superior de IngenieríaUniversidad MálagaMálagaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-77610-1
  • Copyright Information Springer-Verlag US 2008
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-77609-5
  • Online ISBN 978-0-387-77610-1
  • Series Print ISSN 1387-666X
  • About this book