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Evolutionary Algorithms and Agricultural Systems

  • Book
  • © 2002

Overview

Authors:
  1. David G. Mayer

    1. Queensland Beef Industry Institute, Australia

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 647)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-ix
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  2. Rationale for Systems Modelling

    • David G. Mayer
    Pages 1-7

  3. Agricultural Systems Models

    • David G. Mayer
    Pages 9-17

  4. Application of Evolutionary Algorithms to Models

    • David G. Mayer
    Pages 19-31

  5. Applications of Alternate Optimisation Techniques

    • David G. Mayer
    Pages 33-52

  6. Comparisons of Optimisation Techniques

    • David G. Mayer
    Pages 53-60

  7. Robust Parameters for Evolutionary Algorithms

    • David G. Mayer
    Pages 61-75

  8. Future Developments

    • David G. Mayer
    Pages 77-78

  9. Back Matter

    Pages 79-107
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Keywords

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.

Authors and Affiliations

  • Queensland Beef Industry Institute, Australia

    David G. Mayer

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