A tolerance-based memetic algorithm for constrained covering array generation

Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final so...
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Autor*in:	Guo, Xu [verfasserIn] Song, Xiaoyu Zhou, Jian-tao Wang, Feiyu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Combinatorial testing Constrained covering array generation (CCAG) Memetic algorithm Tolerance strategy

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Memetic computing - Berlin : Springer, 2009, 15(2023), 3 vom: 29. Aug., Seite 319-340
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:3 ; day:29 ; month:08 ; pages:319-340

Links:	Volltext

DOI / URN:	10.1007/s12293-023-00392-1

Katalog-ID:	SPR053012011

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520			\|a Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems.
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650		4	\|a Tolerance strategy \|7 (dpeaa)DE-He213
700	1		\|a Song, Xiaoyu \|4 aut
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700	1		\|a Wang, Feiyu \|4 aut
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allfields	10.1007/s12293-023-00392-1 doi (DE-627)SPR053012011 (SPR)s12293-023-00392-1-e DE-627 ger DE-627 rakwb eng Guo, Xu verfasserin aut A tolerance-based memetic algorithm for constrained covering array generation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. Combinatorial testing (dpeaa)DE-He213 Constrained covering array generation (CCAG) (dpeaa)DE-He213 Memetic algorithm (dpeaa)DE-He213 Tolerance strategy (dpeaa)DE-He213 Song, Xiaoyu aut Zhou, Jian-tao aut Wang, Feiyu aut Enthalten in Memetic computing Berlin : Springer, 2009 15(2023), 3 vom: 29. Aug., Seite 319-340 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:15 year:2023 number:3 day:29 month:08 pages:319-340 https://dx.doi.org/10.1007/s12293-023-00392-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 3 29 08 319-340
spelling	10.1007/s12293-023-00392-1 doi (DE-627)SPR053012011 (SPR)s12293-023-00392-1-e DE-627 ger DE-627 rakwb eng Guo, Xu verfasserin aut A tolerance-based memetic algorithm for constrained covering array generation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. Combinatorial testing (dpeaa)DE-He213 Constrained covering array generation (CCAG) (dpeaa)DE-He213 Memetic algorithm (dpeaa)DE-He213 Tolerance strategy (dpeaa)DE-He213 Song, Xiaoyu aut Zhou, Jian-tao aut Wang, Feiyu aut Enthalten in Memetic computing Berlin : Springer, 2009 15(2023), 3 vom: 29. Aug., Seite 319-340 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:15 year:2023 number:3 day:29 month:08 pages:319-340 https://dx.doi.org/10.1007/s12293-023-00392-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 3 29 08 319-340
allfields_unstemmed	10.1007/s12293-023-00392-1 doi (DE-627)SPR053012011 (SPR)s12293-023-00392-1-e DE-627 ger DE-627 rakwb eng Guo, Xu verfasserin aut A tolerance-based memetic algorithm for constrained covering array generation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. Combinatorial testing (dpeaa)DE-He213 Constrained covering array generation (CCAG) (dpeaa)DE-He213 Memetic algorithm (dpeaa)DE-He213 Tolerance strategy (dpeaa)DE-He213 Song, Xiaoyu aut Zhou, Jian-tao aut Wang, Feiyu aut Enthalten in Memetic computing Berlin : Springer, 2009 15(2023), 3 vom: 29. Aug., Seite 319-340 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:15 year:2023 number:3 day:29 month:08 pages:319-340 https://dx.doi.org/10.1007/s12293-023-00392-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 3 29 08 319-340
allfieldsGer	10.1007/s12293-023-00392-1 doi (DE-627)SPR053012011 (SPR)s12293-023-00392-1-e DE-627 ger DE-627 rakwb eng Guo, Xu verfasserin aut A tolerance-based memetic algorithm for constrained covering array generation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. Combinatorial testing (dpeaa)DE-He213 Constrained covering array generation (CCAG) (dpeaa)DE-He213 Memetic algorithm (dpeaa)DE-He213 Tolerance strategy (dpeaa)DE-He213 Song, Xiaoyu aut Zhou, Jian-tao aut Wang, Feiyu aut Enthalten in Memetic computing Berlin : Springer, 2009 15(2023), 3 vom: 29. Aug., Seite 319-340 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:15 year:2023 number:3 day:29 month:08 pages:319-340 https://dx.doi.org/10.1007/s12293-023-00392-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 3 29 08 319-340
allfieldsSound	10.1007/s12293-023-00392-1 doi (DE-627)SPR053012011 (SPR)s12293-023-00392-1-e DE-627 ger DE-627 rakwb eng Guo, Xu verfasserin aut A tolerance-based memetic algorithm for constrained covering array generation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. Combinatorial testing (dpeaa)DE-He213 Constrained covering array generation (CCAG) (dpeaa)DE-He213 Memetic algorithm (dpeaa)DE-He213 Tolerance strategy (dpeaa)DE-He213 Song, Xiaoyu aut Zhou, Jian-tao aut Wang, Feiyu aut Enthalten in Memetic computing Berlin : Springer, 2009 15(2023), 3 vom: 29. Aug., Seite 319-340 (DE-627)597545006 (DE-600)2489140-X 1865-9292 nnns volume:15 year:2023 number:3 day:29 month:08 pages:319-340 https://dx.doi.org/10.1007/s12293-023-00392-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 15 2023 3 29 08 319-340
language	English
source	Enthalten in Memetic computing 15(2023), 3 vom: 29. Aug., Seite 319-340 volume:15 year:2023 number:3 day:29 month:08 pages:319-340
sourceStr	Enthalten in Memetic computing 15(2023), 3 vom: 29. Aug., Seite 319-340 volume:15 year:2023 number:3 day:29 month:08 pages:319-340
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Combinatorial testing Constrained covering array generation (CCAG) Memetic algorithm Tolerance strategy
isfreeaccess_bool	false
container_title	Memetic computing
authorswithroles_txt_mv	Guo, Xu @@aut@@ Song, Xiaoyu @@aut@@ Zhou, Jian-tao @@aut@@ Wang, Feiyu @@aut@@
publishDateDaySort_date	2023-08-29T00:00:00Z
hierarchy_top_id	597545006
id	SPR053012011
language_de	englisch
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author	Guo, Xu
spellingShingle	Guo, Xu misc Combinatorial testing misc Constrained covering array generation (CCAG) misc Memetic algorithm misc Tolerance strategy A tolerance-based memetic algorithm for constrained covering array generation
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topic_title	A tolerance-based memetic algorithm for constrained covering array generation Combinatorial testing (dpeaa)DE-He213 Constrained covering array generation (CCAG) (dpeaa)DE-He213 Memetic algorithm (dpeaa)DE-He213 Tolerance strategy (dpeaa)DE-He213
topic	misc Combinatorial testing misc Constrained covering array generation (CCAG) misc Memetic algorithm misc Tolerance strategy
topic_unstemmed	misc Combinatorial testing misc Constrained covering array generation (CCAG) misc Memetic algorithm misc Tolerance strategy
topic_browse	misc Combinatorial testing misc Constrained covering array generation (CCAG) misc Memetic algorithm misc Tolerance strategy
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title	A tolerance-based memetic algorithm for constrained covering array generation
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title_full	A tolerance-based memetic algorithm for constrained covering array generation
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author_browse	Guo, Xu Song, Xiaoyu Zhou, Jian-tao Wang, Feiyu
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title_sort	tolerance-based memetic algorithm for constrained covering array generation
title_auth	A tolerance-based memetic algorithm for constrained covering array generation
abstract	Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract The research focus in the field of combinatorial testing has shifted from unconstrained covering array generation (CAG) to constrained covering array generation (CCAG). Most heuristic search algorithms exclude all invalid solutions from the search space so that all intermediate and final solutions satisfy the constraints. However, some values of intermediate solutions that are sometimes invalid may help in finding the optimal solution. Therefore, such solutions may need to be "tolerated" in the generation process. This paper proposes a tolerance-based memetic algorithm named QSMA that employs quantum particle swarm optimization (QPSO) as a global searching operator and improved simulated annealing as a local searching operator to balance exploration and exploitation synergistically. Meanwhile, QSMA incorporates a stochastic ranking strategy to address the challenge of setting the penalty coefficient. Besides, a specific penalty function is proposed to deal with constraints, and design guidelines for penalty functions are suggested. In the experiment, the impacts of parameter settings on the performance of QSMA are investigated. Extensive experimental results show that the QSMA algorithm outperforms other methods and tools for both CAG and CCAG problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	A tolerance-based memetic algorithm for constrained covering array generation
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A tolerance-based memetic algorithm for constrained covering array generation

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