CBGA-ES+: A Cluster-Based Genetic Algorithm with Non-Dominated Elitist Selection for Supporting Multi-Objective Test Optimization

• Published in: IEEE transactions on software engineering Vol. 47; no. 1; pp. 86 - 107
• Main Authors: Pradhan, Dipesh, Wang, Shuai, Ali, Shaukat, Yue, Tao, Liaaen, Marius
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2021; IEEE Computer Society
• Subjects: Clustering algorithms; Clusters; Elitism; Elitist selection; Evolutionary algorithms; Genetic algorithms; multi-objective genetic algorithm; multi-objective test optimization; Multiple objective analysis; Optimization; Randomness; search; Search algorithms; Search problems; Sociology; Software algorithms; Sorting algorithms; Statistics
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/TSE.2018.2882176

Many real-world test optimization problems (e.g., test case prioritization) are multi-objective intrinsically and can be tackled using various multi-objective search algorithms (e.g., Non-dominated Sorting Genetic Algorithm (NSGA-II)). However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In a worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the solutions in the next generation. To address such a challenge, we propose CBGA-ES + , a novel cluster-based genetic algorithm with non-dominated elitist selection to reduce the randomness when selecting the parent solutions to support multi-objective test optimization. We empirically compared CBGA-ES + with random search and greedy (as baselines), four commonly used multi-objective search algorithms (i.e., Multi-objective Cellular genetic algorithm (MOCell), NSGA-II, Pareto Archived Evolution Strategy (PAES), and Strength Pareto Evolutionary Algorithm (SPEA2)), and the predecessor of CBGA-ES + (named CBGA-ES) using five multi-objective test optimization problems with eight subjects (two industrial, one real world, and five open source). The results showed that CBGA-ES + managed to significantly outperform the selected search algorithms for a majority of the experiments. Moreover, for the solutions in the same search space, CBGA-ES + managed to perform better than CBGA-ES, MOCell, NSGA-II, PAES, and SPEA2 for 2.2, 13.6, 14.5, 17.4, and 9.9 percent, respectively. Regarding the running time of the algorithm, CBGA-ES + was faster than CBGA-ES for all the experiments.