A new approach to distribute MOEA pareto front computation

Abstract

Multi-Objective Evolutionary Algorithms (MOEAs) offer compelling solutions to many real world problems, including software engineering ones. However, their efficiency decreases with the growing size of the problems at hand, hindering their applicability in practice. In this paper we propose a novel master-worker approach to distribute the computation of the Pareto Front (PF) for MOEAs (dubbed MOEA-DPF) and empirically evaluate it on a real-world software project management problem. With respect to previous work, our proposal can be used with any MOEA to tackle multiobjective problems regardless of their formulation/representation. Our results show that MOEA-DPF runs significantly faster (up to 3.1x speed-up using two workers) than its sequential counterpart while maintaining (and even improving) the quality of the PF. We conclude that MOEA-DPF provides an effective and simple solution to speed-up the execution of MOEAs by distributing the PF computation, making them effective for real-world problems. CCS CONCEPTS • Software and its engineering → Search-based software engineering; • Computing methodologies → Distributed algorithms;

Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction Strategies

“…In any case, if the testers find themselves in at least one of those two situations, then the training cost of Sentinel can be a good price to pay for the significant mutant reduction trade-off provided by the generated strategies, specially considering that doing an exhaustive manual experimentation to find the best conventional strategy was more expensive than Sentinel training for six out of 10 systems (as seen in Section 5.3). Moreover, Sentinel's training cost can be further reduced by running it in parallel [60], [61], [62], [63], [64], [65], [66].…”