State of Art in Genetic Algorithms for Agricultural Systems

  • Sorana D. BOLBOACĂ "Iuliu Hațieganu" University of Medicine and Pharmacy Cluj-Napoca, Faculty of Medicine, 13 Emil Isac, 400023 Cluj-Napoca
  • Lorentz JÄNTSCHI Technical University of Cluj-Napoca, Department of Chemistry, 103-105 Muncii Bvd, 400641
  • Mugur C. BĂLAN "Babeș-Bolyai" University, 11 Arany Janos, 400028 Cluj-Napoca
  • Mircea V. DIUDEA "Babeș-Bolyai" University, 11 Arany Janos, 400028 Cluj-Napoca
  • Radu E. SESTRAS University of Agricultural Sciences and Veterinary Medicine, Cluj-Napoca, 3-5 Manastur St., 400372

Abstract

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.

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Published
2010-12-05
How to Cite
BOLBOACĂ, S. D., JÄNTSCHI, L., BĂLAN, M. C., DIUDEA, M. V., & SESTRAS, R. E. (2010). State of Art in Genetic Algorithms for Agricultural Systems. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 38(3), 51-63. https://doi.org/10.15835/nbha3835455
Section
Research Articles

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