Citation:
D. J. Parsons and D. Te Beest, Optimising Fungicide Applications on Winter Wheat using Genetic Algorithms, Biosystems Engineering, Volume 88, Issue 4, August 2004, Pages 401-410.
Abstract:
A genetic algorithm is used in a decision support system to select the
combinations of chemicals and the timing of successive treatments for the
optimal control of fungal diseases in winter wheat crops, using a simulation
model to predict the performance of different treatments. The search space is
large and discrete, making the use of conventional optimisation methods
impractical. Furthermore, the user requirements specify that the method must
supply lists of near-optimal solutions, which fits with the use of populations
of solutions in the genetic algorithm. Substantial improvements in the
performance of the algorithm were obtained by tuning the fitness, selection,
reproduction and replacement methods for the optimisation of short-term and
long-term decisions. These also ensured rapid convergence in the former and
prevented premature convergence in the latter. The algorithm has proved to be
effective at finding optimal and near optimal solutions within an acceptable
time. When compared with exhaustive searches for cases where this is possible
(short-term planning with restricted choices), it typically finds 5–8 of the top
10 plans and a similar number of the next 10. The results of the system in field
and user trials have been goo
