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.
引用
收藏
页码：215 / 226
页数：11
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