Optimized Differential Evolution Algorithm for Software Testing

被引：0
|
作者：
Xiaodong Gou
Tingting Huang
Shunkun Yang
Mengxuan Su
Fuping Zeng
机构：
[1] Beihang University,School of Reliability and Systems Engineering
[2] University of Melbourne,undefined
来源：
International Journal of Computational Intelligence Systems | 2018年 / 12卷
关键词：
Software testing; Test data generation; Differential evolution algorithm; Premature convergence; Anti-aging; Rebirth strategy;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Differential evolution (DE) algorithms for software testing usually exhibited limited performance and stability owing to possible premature-convergence-related aging during evolution processes. This paper proposes a new framework comprising an antiaging mechanism, that is, a rebirth strategy with partial memory against aging, for the existing DE algorithm and a specialized fitness function. The results of application of the proposed framework to instantiate three DE algorithms with different mutation schemas indicate that it significantly improved their effectiveness, performance, and stability.
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收藏
页码：215 / 226
页数：11
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