Evaluating Search-Based Techniques With Statistical Tests

This tutorial covers the basics of how to use statistical tests to evaluate and compare search-algorithms, in particular when applied on software engineering problems. Search-algorithms like Hill Climbing and Genetic Algorithms are randomised. Running such randomised algorithms twice on the same problem can give different results. It is hence important to run such algorithms multiple times to collect average results, and avoid so publishing wrong conclusions that were based on just luck. However, there is the question of how often such runs should be repeated. Given a set of n repeated experiments, is such n large enough to draw sound conclusions? Or should had more experiments been run? Statistical tests like the Wilcoxon-Mann-Whitney U-test can be used to answer these important questions