Evolutionary Algorithms and Agricultural Systems

  • David G. Mayer

Table of contents

  1. Front Matter
    Pages i-ix
  2. David G. Mayer
    Pages 1-7
  3. David G. Mayer
    Pages 9-17
  4. David G. Mayer
    Pages 53-60
  5. David G. Mayer
    Pages 77-78
  6. Back Matter
    Pages 79-107

About this book


Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems.
Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies.
Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.


algorithms evolution evolutionary algorithm genetic algorithms modeling operations research optimization search strategy

Authors and affiliations

  • David G. Mayer
    • 1
  1. 1.Queensland Beef Industry InstituteAustralia

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5693-6
  • Online ISBN 978-1-4615-1717-7
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site