Novel Genetic Algorithm Crossover Approaches for Time-Series Problems
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چکیده
Genetic Algorithms (GAs) are a commonly used stochastic search heuristic which have been applied to a plethora of problem domains. GAs work on a population of chromosomes (an encoding of a solution to the problem at hand) and breed solutions from fit parents to hopefully produce fitter children through a process of crossover and mutation. This work discusses two novel crossover approaches for GAs when applied to the optimisation of time-series problems, with particular application to bio-control schedules.
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تاریخ انتشار 2007