Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultu ral systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with liter ature and our studies identifying evolutionary algorithms (including geneti c algorithms) as superior in this regard to simulated annealing, tabu searc h, hill-climbing, and direct-search methods. Results of a complex beef prop erty optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultu ral systems. (C) 2001 Elsevier Science Ltd. All rights reserved.