Multi-objective optimisation for regression testing

Multi-objective optimisation for regression testing

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-obje...

Full Elite Sets for Multi-objective Optimisation

Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of fronta...

Multi-Objective Optimisation for Information Access Tasks

Information access tasks involve setting a large number of model parameters (such as term weighting functions or the particular terms from the document collection to use in the model); in addition, information access tasks frequently attempt to satisfy multiple competing objectives, such as precision and recall. We investigate the use of multi-objective evolutionary algorithms for finding model...

Multi-objective Binary Search Optimisation

Multi-objective particle swarm optimisation methods

This study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with ‘distance’ based gbest selection techniques. Theoretic...