Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Crops selection for optimal soil planning using multiobjective evolutionary algorithms
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Crop rotation is a cropping system alternative that can reduce agriculture's dependence on external inputs through internal nutrient recycling. Also, it maintains long-term productivity of lands and breaks weed and disease cycles. Decision criteria to choose among competing crop rotation systems include economic and environmental considerations. Having many cultivation parcels, selection of optimal rotation alternatives may become difficult as different issues have to be analyzed simultaneously. Thus, this work proposes to use Multiobjective Evolutionary Algorithms (MOEA) to solve a multi-objective crop rotation optimization problem considering various parcels and objectives. Three outstanding MOEAs were implemented: the Strength Pareto Evolutionary Algorithm 2, the Non-dominated Sorting Genetic Algorithm and the micro-Genetic Algorithm. These MOEAS were tested using real data and their results compared using a set of metrics. The provided results have shown to be potentially useful for decision making support.