A genetic algorithm approach to multiobjective land use planning
Computers and Operations Research
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Determining Optimal Crop Rotations by Using Multiobjective Evolutionary Algorithms
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
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Farm managers have to deal with many conflicting objectives when planning which crop to cultivate. Soil characteristics are extremely important when determining yield potential. Fertilization and liming are commonly used to adapt soils to the nutritional requirements of the crops to be cultivated. Planting the crop that will best fit the soil characteristics is an interesting alternative to minimize the need for soil treatment, reducing costs and potential environmental damages. In addition farmers usually look for investments that offer the greatest potential earnings with the least possible risks. According to the objectives to be considered the crop selection problem can be difficult to solve using traditional tools. Therefore, this work proposes an approach based on Multiobjective Evolutionary Algorithms to help in the selection of an appropriate cultivation plan considering five crop alternatives and five objectives simultaneously.