The Genetic Algorithm

A typical GA does its tuning in stages called generations. Usually, the average fitness of individuals will increase with each generation, through the process of natural selection. We start with a random collection of individuals. In each successive generation, individuals with bad genes are weeded out while those with good genes propagate their genetic code. The genetic code that determines the fitness of an individual is termed, logically enough, the chromosome of that individual. Given a chromosome, the GA should be able to ascertain its fitness. In our case, this is done by performing the BWER detection analysis on all the truthed cases in the verification database using the chromosome and finding the skill score of the resulting detections.

In our GA, each chromosome consists of a fixed number of genes. Although there are applications that use chromosomes of varying length (see Koza (1992) for example), it is overkill in weather detection algorithms where we already know the form of the solution or, at least, the shape of the membership functions of all the fuzzy sets. Since we can identify the closed list of features that we wish to utilize in our solution, a chromosome that assigns a gene to each feature will suffice.