Conclusion

The paradigm of using a genetic algorithm to tune a weather detection algorithm is useful because genetic algorithms work in the same space as the original problem, removing the need to compute attributes such as gradients that are needed for optimizing by other means. It also provides a convenient way to transfer the results of optimization to a run-time algorithm since all the optimizable information is in a chromosome. Chromosomes tuned to a particular location or types of weather events can be swapped for one another, leading to more customizable algorithms. For example, the BWER detection algorithm could be customized very easily for the differing needs of forecasters and a neural network by simply changing the fitness function and retuning the algorithm. The run-time algorithm is the same; only the chromosome that it uses for the two end-users is different.