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© 2010

Bioinspired Computation in Combinatorial Optimization

Algorithms and Their Computational Complexity

Benefits

  • Authors have given tutorials on this topic at major international conferences

  • Text has been class-tested by the authors and their collaborators

  • Comprehensive introduction for researchers

Textbook
  • 16k Downloads

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Basics

    1. Front Matter
      Pages 1-1
    2. Frank Neumann, Carsten Witt
      Pages 3-7
    3. Frank Neumann, Carsten Witt
      Pages 9-19
    4. Frank Neumann, Carsten Witt
      Pages 21-32
    5. Frank Neumann, Carsten Witt
      Pages 33-48
  3. Single-objective Optimization

    1. Front Matter
      Pages 49-49
    2. Frank Neumann, Carsten Witt
      Pages 51-74
    3. Frank Neumann, Carsten Witt
      Pages 75-94
    4. Frank Neumann, Carsten Witt
      Pages 95-110
    5. Frank Neumann, Carsten Witt
      Pages 111-131
    6. Frank Neumann, Carsten Witt
      Pages 133-146
  4. Multi-objective Optimization

    1. Front Matter
      Pages 147-147
    2. Frank Neumann, Carsten Witt
      Pages 149-159
    3. Frank Neumann, Carsten Witt
      Pages 161-169
    4. Frank Neumann, Carsten Witt
      Pages 171-189
    5. Frank Neumann, Carsten Witt
      Pages 191-203
  5. Back Matter
    Pages 205-216

About this book

Introduction

Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these search heuristics. This is the first book to explain the most important results achieved in this area.

The authors show how runtime behavior can be analyzed in a rigorous way. in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.

This book will be valuable for graduate and advanced undergraduate courses on bioinspired computation, as it offers clear assessments of the benefits and drawbacks of various methods. It offers a self-contained presentation, theoretical foundations of the techniques, a unified framework for analysis, and explanations of common proof techniques, so it can also be used as a reference for researchers in the areas of natural computing, optimization and computational complexity.

 

Keywords

Bioinspired computing Computational complexity Evolutionary algorithms Minimum spanning trees Multiobjective optimization Natural computing Optimization algorithms combinatorial optimization evolutionary algorithm multi-objective optimization scheduling

Authors and affiliations

  1. 1.Dept. of Algorithms and ComplexityMax Planck Institute for InformaticsSaarbrückenGermany
  2. 2., Department of InformaticsTechnical University of DenmarkKgs. LyngbyDenmark

About the authors

Authors have given tutorials on this topic at major international conferences

Bibliographic information

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Reviews

“A very nice and, with respect to the topics treated, a useful contribution to the literature. The book gives a very appealing introduction into the area of bio-inspired algorithms with solid results on the theoretical side, gathering many recent results which so far only have been available in research papers. … recommendable resource both for researchers who want to learn more on the topic and for preparing a course on bio-inspired algorithms. … Altogether this is a very recommendable textbook.” (Klaus Meer, Mathematical Reviews, February, 2015)

"This timely book will be useful to many researchers and advanced undergraduate and graduate students. The key strength of the book is the complexity analysis of the algorithms for a variety of combinatorial optimization problems on graphs. Furthermore, it provides a comprehensive treatment of evolutionary algorithms and ant colony optimization. The book is recommended to anyone working in the areas of computational complexity, combinatorial optimization, and engineering." (Manish Gupta, Computing Reviews, May, 2011)

“This book treats bio-inspired computing methods as stochastic algorithms and presents rigorous results on their runtime behavior. The book is meant to give researchers a state-of-the-art presentation of theoretical results on bio-inspired computing methods in the context of combinatorial optimization. It can be used as basic material for courses on bio-inspired computing that are meant for graduate students and advanced undergraduates.” (I. N. Katz, Zentralblatt MATH, Vol. 1223, 2011)