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

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...

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Veröffentlicht in:IEEE transactions on software engineering Jg. 47; H. 1; S. 86 - 107
Hauptverfasser: Pradhan, Dipesh, Wang, Shuai, Ali, Shaukat, Yue, Tao, Liaaen, Marius
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2021
IEEE Computer Society
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2018.2882176