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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on software engineering Ročník 47; číslo 1; s. 86 - 107
Hlavní autoři:	Pradhan, Dipesh, Wang, Shuai, Ali, Shaukat, Yue, Tao, Liaaen, Marius
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2021 IEEE Computer Society
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!