期刊
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
卷 47, 期 1, 页码 86-107出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSE.2018.2882176
关键词
Elitist selection; multi-objective genetic algorithm; multi-objective test optimization; search
资金
- Research Council of Norway (RCN) [203461/O30, 240024/F20]
The study introduces CBGA-ES+, a novel cluster-based genetic algorithm with non-dominated elitist selection to reduce randomness in selecting parent solutions for multi-objective test optimization. Empirical comparisons showed that CBGA-ES+ outperformed other selected search algorithms for the majority of experiments. Additionally, CBGA-ES+ performed better than CBGA-ES, MOCell, NSGA-II, PAES, and SPEA2 in the same search space.
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.
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