A Brief Introduction to Continuous Evolutionary Optimization

  • Oliver Kramer

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Intelligence book sub series (BRIEFSINTELL)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Foundations

    1. Front Matter
      Pages 1-1
    2. Oliver Kramer
      Pages 3-14
    3. Oliver Kramer
      Pages 15-26
    4. Oliver Kramer
      Pages 27-34
  3. Advanced Optimization

    1. Front Matter
      Pages 35-35
    2. Oliver Kramer
      Pages 37-44
    3. Oliver Kramer
      Pages 45-54
    4. Oliver Kramer
      Pages 55-64
  4. Learning

    1. Front Matter
      Pages 65-65
    2. Oliver Kramer
      Pages 67-76
    3. Oliver Kramer
      Pages 77-85
  5. Back Matter
    Pages 87-94

About this book

Introduction

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator, and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.

Keywords

Computational Intelligence Continuous Evolutionary Optimization Evolutionary Optimization

Authors and affiliations

  • Oliver Kramer
    • 1
  1. 1.Department für InformatikCarl von Ossietzky University of OldenburgOldenburgGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-03422-5
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-03421-8
  • Online ISBN 978-3-319-03422-5
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • About this book
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