Volume 12, Issue 1, November 2018, Pages 215 - 226
Optimized Differential Evolution Algorithm for Software Testing
Received 15 August 2018, Accepted 4 November 2018, Available Online 19 November 2018.
- DOI
- 10.2991/ijcis.2018.125905642How to use a DOI?
- Keywords
- Software testing; Test data generation; Differential evolution algorithm; Premature convergence; Anti-aging; Rebirth strategy
- Abstract
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.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Xiaodong Gou AU - Tingting Huang AU - Shunkun Yang AU - Mengxuan Su AU - Fuping Zeng PY - 2018 DA - 2018/11/19 TI - Optimized Differential Evolution Algorithm for Software Testing JO - International Journal of Computational Intelligence Systems SP - 215 EP - 226 VL - 12 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2018.125905642 DO - 10.2991/ijcis.2018.125905642 ID - Gou2018 ER -