State of Art in Genetic Algorithms for Agricultural Systems
Keywords:genetic algorithm; agricultural system; agro-economic system
Genetic algorithms are built as abstract populations of a number of candidate solutions, each of it being evaluated for accomplish a desired performance. Populations evolve from one generation to another through mutation, crossover and selection in order to obtain an acceptable solution. Genetic algorithms applications cover the subject of decision, classification, optimization and simulation of hard problems. The quality of a genetic algorithm is evaluated in terms of speed, accuracy and domain of applicability. The use of all genetic operators could assure the convergence towards the optimum solution for a specific hard problem. The approaches used to construct the search space and the objective function (survival of the fittest, natural selection) assure the diversity of genetic algorithms. Studies on the development and use of genetic algorithms in solving hard problems in the field of agricultural systems were identified, analyzed and are presented here.
How to Cite
Open Access Journal:
The journal allows the author(s) to retain publishing rights without restriction. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author.