Page 142 - 《软件学报》2025年第5期
P. 142
2042 软件学报 2025 年第 36 卷第 5 期
[25] Wang HD, Jin YC, Sun CL, Doherty J. Offline data-driven evolutionary optimization using selective surrogate ensembles. IEEE Trans.
on Evolutionary Computation, 2019, 23(2): 203–216. [doi: 10.1109/TEVC.2018.2834881]
[26] Lin QZ, Wu XF, Ma LJ, Li JQ, Gong MG, Coello CAC. An ensemble surrogate-based framework for expensive multiobjective
evolutionary optimization. IEEE Trans. on Evolutionary Computation, 2022, 26(4): 631–645. [doi: 10.1109/TEVC.2021.3103936]
[27] Gong DW, Sun BC, Yao XJ, Tian T. Test data generation for path coverage of MPI programs using SAEO. ACM Trans. on Software
Engineering and Methodology, 2021, 30(2): 17. [doi: 10.1145/3423132]
[28] Song ZS, Wang HD, He C, Jin YC. A Kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization.
IEEE Trans. on Evolutionary Computation, 2021, 25(6): 1013–1027. [doi: 10.1109/TEVC.2021.3073648]
[29] Tan Z, Wang HD. A kriging-assisted evolutionary algorithm using feature selection for expensive sparse multi-objective optimization. In:
Proc. of the 2020 IEEE Congress on Evolutionary Computation. Glasgow: IEEE, 2020. 1–8. [doi: 10.1109/CEC48606.2020.9185825]
[30] Sun BC, Gong DW, Pan F, Yao XJ, Tian T. Evolutionary generation of test suites for multi-path coverage of MPI programs with non-
determinism. IEEE Trans. on Software Engineering, 2023, 49(6): 3504–3523. [doi: 10.1109/TSE.2023.3263509]
[31] Li HB, Li SH, Benavides Z, Chen ZZ, Gupta R. COMPI: Concolic testing for MPI applications. In: Proc. of the 2018 IEEE Int’l Parallel
and Distributed Processing Symp. Vancouver: IEEE, 2018. 865–874. [doi: 10.1109/IPDPS.2018.00096]
[32] Davies T, Karlsson C, Liu H, Ding C, Chen ZZ. High performance linpack benchmark: A fault tolerant implementation without
checkpointing. In: Proc. of the 2011 Int’l Conf. on Supercomputing. Tucson: ACM, 2011. 162–171. [doi: 10.1145/1995896.1995923]
[33] Schaich D, DeGrand T. Parallel software for lattice N=4 supersymmetric Yang–Mills theory. Computer Physics Communications, 2015,
190: 200–212. [doi: 10.1016/j.cpc.2014.12.025]
[34] Yu HB. Combining symbolic execution and model checking to verify MPI programs. In: Proc. of the 40th Int’l Conf. on Software
Engineering: Companion. Gothenburg: IEEE, 2018. 527–529.
[35] Darling AE, Carey L, Feng WC. The design, implementation, and evaluation of mpiBLAST. In: Proc. of ClusterWorld Conf. & Expo and
the 4th Int’l Conf. on Linux Clusters: The HPC Revolution. 2003. 13–15.
[36] Igel C, Heidrich-Meisner V, Glasmachers T. Shark. The Journal of Machine Learning Research, 2008, 9: 993–996.
[37] Das S, Suganthan PN. Differential evolution: A survey of the state-of-the-art. IEEE Trans. on Evolutionary Computation, 2011, 15(1):
4–31. [doi: 10.1109/TEVC.2010.2059031]
[38] Brest J, Greiner S, Boskovic B, Mernik M, Zumer V. Self-adapting control parameters in differential evolution: A comparative study on
numerical benchmark problems. IEEE Trans. on Evolutionary Computation, 2006, 10(6): 646–657. [doi: 10.1109/TEVC.2006.872133]
孙百才(1990-), 男, 博士, CCF 专业会员, 主要 姚香娟(1975-), 女, 博士, 教授, 博士生导师,
研究领域为智能软件工程, 机器学习. CCF 高级会员, 主要研究领域为变异测试, 运筹
优化.
巩敦卫(1970-), 男, 博士, 教授, 博士生导师,
CCF 杰出会员, 主要研究领域为智能软件工程,
智能优化理论及应用.