Evolutionary Algorithms and Agricultural Systems

Abstract

Evolutionary Algorithms and Agricultural Systems deals with the practicalapplication of evolutionary algorithms to the study and managementof agricultural systems. The rationale of systems research methodologyis introduced, and examples listed of real-world applications. Itis the integration of these agricultural systems models with optimizationtechniques, primarily genetic algorithms, which forms the focus ofthis book. The advantages are outlined, with examples of agriculturalmodels ranging from national and industry-wide studies down to thewithin-farm scale. The potential problems of this approach are alsodiscussed, 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. Theparticular problems and methodologies of these algorithms, includingadvantageous features that may benefit a hybrid approach or be usefullyincorporated into evolutionary algorithms, are outlined. From considerationof this and the published examples, it is concluded that evolutionaryalgorithms are the superior method for the practical optimizationof models of agricultural and natural systems. General recommendationson robust options and parameter settings for evolutionary algorithmsare given for use in future studies.Evolutionary Algorithms and Agricultural Systems will prove usefulto practitioners and researchers applying these methods to the optimizationof agricultural or natural systems, and would also be suited as atext for systems management, applied modeling, or operations research.