Evolutionary computing for optimizing a regions distribution of agricultural production

This paper describes a GIS-based software package that incorporates a genetic algorithm to optimize crops distribution across any region. Such optimization is powered by maps of where one finds the most suitable conditions for each crop, or each crops current local yields, market price, market demand or transport costs. Our programs output is the crops distribution which achieves maximum economic return, or minimal environmental damage, or optimal fit with either present- or post-climatic-change soil suitability or minimum transport cost. The package can be implemented within any region where the necessary input data exists in Ascii and image format, and it incorporates a number of features that make it transparent and flexible. Such user friendliness encourages even laypersons to experiment with the genetic algorithms parameters, almost as if they are playing a computer game, to see whether or not they can find an even more optimal crops distribution than they found previously. The package also functions as a useful exploratory tool for seeing how current patterns would have to be modified if a more optimal crops distribution were achieved, thereby generating decision support type insights into possible repercussions of tampering with the status quo. Our packages functionality will be demonstrated through a case study implementation within the agricultural region of South Gippsland, Australia.