Bridging the Gap Between Theory and Practice

While the gap between theory and practice is slowly closing, the evolutionary computation community needs to concentrate more heavily on the middle ground. This paper defends the position that contemporary analytical tools facilitate such a concentration. Empirical research can be improved by considering modern analytical techniques in experimental design. In addition, formal analytical extensions of empirical works are possible. We justify our position by way of a constructive example: we consider a recent empirically-based research paper and extend it using modern techniques of asymptotic analysis of run time performance of the algorithms and problems investigated in that paper. The result is a more general understanding of the performance of these algorithms for any size of input, as well as a better understanding of the underlying reasons for some of the previous results. Moreover, our example points out how important it is that empirical researchers motivate their parameter choices more clearly. We believe that providing theorists with empirical studies that are well-suited for formal analysis will help bridge the gap between theory and practice, benefitting the empiricist, the theorist, and the community at large.