The objective of this work is to implement a new hybrid feature selection system comprised of a genetic algorithm (GA) and a support vector machine program termed SVM^perf. We have used this system to perform feature reduction of a colorectal cancer microarray dataset generated by the Moffitt Cancer Center. Using variance pruning as a coarse feature selection process with the GA-SVM^perf wrapper, the method provided an A_z (performance measure) value of .97 with only 7 features after 30 generations. Using a combination of variance pruning, t-tests and the GA-SVM^perf wrapper, the method provided an A...