Tuning multi-objective optimization algorithms for the integration and testing order problem

Abstract

Multi-Objective Evolutionary Algorithms (MOEAs) are one of the most used search techniques in Search-Based Software Engineering (SBSE). However, MOEAs have many control parameters which must be configured for the problem at hand. This can be a very challenging task by itself. To make matters worse, in Multi-Objective Optimization (MOO) different aspects of quality of the obtained Pareto front need to be taken in to account. A novel method called MOCRS-Tuning is proposed to address this problem. MOCRS-Tuning is a meta-evolutionary algorithm which uses a chess rating system with quality indicator ensemble. The chess rating system enables us to determine the performance of an MOEA on different problems easily. The ensemble of quality indicators ensures that different aspects of quality are considered. The tuning was carried out on five different MOEAs on the Integration and Test Order Problem (ITO). The experimental results show significant improvement after tuning of all five MOEAs used in the experiment.