{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:16:32Z","timestamp":1783577792504,"version":"3.55.0"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032306982","type":"print"},{"value":"9783032306999","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-30699-9_5","type":"book-chapter","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:43:20Z","timestamp":1783575800000},"page":"67-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Zoom, Don\u2019t Wander: Why Regional Search Outperforms Pareto Reasoning and\u00a0Global Optimization In Budget-Constrained SBSE"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4075-0040","authenticated-orcid":false,"given":"Kishan Kumar","family":"Ganguly","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-3196","authenticated-orcid":false,"given":"Tim","family":"Menzies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"5_CR1","unstructured":"Abdullah, M.: Student dropout analysis and prediction dataset (2025), kaggle"},{"key":"5_CR2","doi-asserted-by":"publisher","unstructured":"Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: International Conference on Software Engineering, pp. 1\u201310 (2011). https:\/\/doi.org\/10.1145\/1985793.1985795","DOI":"10.1145\/1985793.1985795"},{"key":"5_CR3","doi-asserted-by":"publisher","unstructured":"Awad, N., Mallik, N., Hutter, F.: DEHB: evolutionary hyberband for scalable, robust and efficient hyperparameter optimization. In: Proc. IJCAI, pp. 2147\u20132153 (2021). https:\/\/doi.org\/10.24963\/ijcai.2021\/296","DOI":"10.24963\/ijcai.2021\/296"},{"issue":"14","key":"5_CR4","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/S0950-5849(01)00194-X","volume":"43","author":"AJ Bagnall","year":"2001","unstructured":"Bagnall, A.J., Rayward-Smith, V.J., Whittley, I.M.: The next release problem. Inf. Soft. Tech. 43(14), 883\u2013890 (2001)","journal-title":"Inf. Soft. Tech."},{"key":"5_CR5","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Proc. NeurIPS, vol. 24 (2011)"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1484","volume":"13","author":"B Bischl","year":"2023","unstructured":"Bischl, B., Binder, M., Lang, M., et al.: Hyperparameter optimization: foundations, algorithms, best practices, and open challenges. WIREs Data Mining Knowl. Discov. 13(2), e1484 (2023). https:\/\/doi.org\/10.1002\/widm.1484","journal-title":"WIREs Data Mining Knowl. Discov."},{"key":"5_CR7","unstructured":"Blastchar: Telco customer churn (2025), kaggle"},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3514233","volume":"32","author":"J Chen","year":"2023","unstructured":"Chen, J., Li, M.: The weights can be harmful: pareto search versus weighted search in multi-objective search-based software engineering. ACM Trans. Soft. Eng. Methodol. 32, 1\u201340 (2023). https:\/\/doi.org\/10.1145\/3514233","journal-title":"ACM Trans. Soft. Eng. Methodol."},{"issue":"6","key":"5_CR9","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/TSE.2018.2790925","volume":"45","author":"J Chen","year":"2018","unstructured":"Chen, J., Nair, V., Krishna, R., Menzies, T.: \u201cSampling\" as a baseline optimizer for search-based software engineering. IEEE Trans. Soft. Eng. 45(6), 597\u2013614 (2018)","journal-title":"IEEE Trans. Soft. Eng."},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.infsof.2017.08.007","volume":"95","author":"J Chen","year":"2018","unstructured":"Chen, J., Nair, V., Menzies, T.: Beyond evolutionary algorithms for search-based software engineering. Inf. Softw. Technol. 95, 281\u2013294 (2018)","journal-title":"Inf. Softw. Technol."},{"key":"5_CR11","unstructured":"Chen, P., Chen, T.: Promisetune: unveiling causally promising and explainable configuration tuning. In: Proc. 48th IEEE\/ACM International Conference on Software Engineering (2026)"},{"issue":"2","key":"5_CR12","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1109\/TSE.2025.3525955","volume":"51","author":"P Chen","year":"2025","unstructured":"Chen, P., Gong, J., Chen, T.: Accuracy can lie: on the impact of surrogate model in configuration tuning. IEEE Trans. Softw. Eng. 51(2), 548\u2013580 (2025)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"5_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2020.106372","volume":"127","author":"TE Colanzi","year":"2020","unstructured":"Colanzi, T.E., Assun\u00e7\u00e3o, W.K.G., Vergilio, S.R., Farah, P.R., Guizzo, G., et al.: The symposium on search-based software engineering: past, present and future. Inf. Softw. Technol. 127, 106372 (2020)","journal-title":"Inf. Softw. Technol."},{"key":"5_CR14","unstructured":"Dansbecker: home data for ml course (2025), kaggle"},{"key":"5_CR15","unstructured":"Dansbecker: Medical data and hospital readmissions (2025), kaggle"},{"key":"5_CR16","unstructured":"Daoud, J.: Marketing analytics - marketing data (2022), kaggle"},{"issue":"4","key":"5_CR17","first-page":"577","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach. TSE 18(4), 577\u2013601 (2014)","journal-title":"TSE"},{"issue":"2","key":"5_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evo. Comp. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evo. Comp."},{"key":"5_CR19","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)"},{"key":"5_CR20","unstructured":"Die9origephit: Fifa world cup 2022: Complete dataset (2025), kaggle"},{"issue":"1","key":"5_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0020-7373(80)80032-0","volume":"13","author":"M Easterby-Smith","year":"1980","unstructured":"Easterby-Smith, M.: The design, analysis and interpretation of repertory grids. Int. J. Man Mach. Stud. 13(1), 3\u201324 (1980)","journal-title":"Int. J. Man Mach. Stud."},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Fraser, G., Arcuri, A.: Evosuite: automatic test suite generation for object-oriented software. In: Proc. Foundations of Software Engineering, pp. 416\u2013419. (2011)","DOI":"10.1145\/2025113.2025179"},{"issue":"1","key":"5_CR23","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1145\/2379776.2379787","volume":"45","author":"M Harman","year":"2012","unstructured":"Harman, M., Mansouri, S.A., Zhang, Y.: Search-based software engineering: trends, techniques and applications. ACM Comput. Surv. 45(1), 11 (2012)","journal-title":"ACM Comput. Surv."},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Hort, M., Sarro, F.: The effect of offspring population size on NSGA-II: a preliminary study. In: Proc. GECCO, pp. 1\u20132. ACM (2021). https:\/\/doi.org\/10.1145\/3449726.3459479","DOI":"10.1145\/3449726.3459479"},{"key":"5_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"5_CR26","unstructured":"Hwang, C.L., Yoon, K.: Multiple attribute decision making: methods and applications a state-of-the-art survey. Springer Science & Business Media (2012)"},{"key":"5_CR27","unstructured":"Jessicali9530: Animal crossing new horizons: Nookplaza dataset (2021), kaggle"},{"issue":"5","key":"5_CR28","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1109\/TSE.2020.3036108","volume":"48","author":"M Li","year":"2022","unstructured":"Li, M., Chen, T., Yao, X.: How to evaluate solutions in pareto-based search-based software engineering: a critical review and methodological guidance. IEEE TSE 48(5), 1771\u20131799 (2022). https:\/\/doi.org\/10.1109\/TSE.2020.3036108","journal-title":"IEEE TSE"},{"key":"5_CR29","doi-asserted-by":"publisher","first-page":"142915","DOI":"10.1109\/ACCESS.2024.3427109","volume":"12","author":"A Lustosa","year":"2024","unstructured":"Lustosa, A., Menzies, T.: iSNEAK: partial ordering as heuristics for model- based reasoning in software engineering. IEEE Access 12, 142915\u2013142929 (2024)","journal-title":"IEEE Access"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Lustosa, A., Menzies, T.: Learning from very little data: on the value of landscape analysis for predicting software project health. ACM Trans. Soft. Eng. Methodol. 33(3), (2024). https:\/\/doi.org\/10.1145\/3630252","DOI":"10.1145\/3630252"},{"key":"5_CR31","unstructured":"Lustosa, A., Menzies, T.: Less noise, more signal: DRR for better optimizations of SE tasks, arXiv preprint arXiv:2503.21086 (2025)"},{"issue":"8","key":"5_CR32","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3746057","volume":"68","author":"T Menzies","year":"2025","unstructured":"Menzies, T.: The case for compact ai. Commun. ACM 68(8), 6\u20137 (2025)","journal-title":"Commun. ACM"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Menzies, T., Ganguly, K.K.: How low can you go? The data-light SE challenge. In: Proc. FSE (2026) (to appear)","DOI":"10.1145\/3808192"},{"key":"5_CR34","doi-asserted-by":"publisher","unstructured":"Menzies, T.: MOOT: a repository of many multi-objective optimization tasks. In: Proc. MSR (2026). https:\/\/doi.org\/10.5281\/zenodo.17354083","DOI":"10.5281\/zenodo.17354083"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Nair, V., et al.: Data-driven search-based software engineering. In: MSR (2018)","DOI":"10.1145\/3196398.3196442"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Nair, V., Menzies, T., Siegmund, N., Apel, S.: Using bad learners to find good configurations. In: Proc. FSE, pp. 257\u2013267 (2017)","DOI":"10.1145\/3106237.3106238"},{"issue":"7","key":"5_CR37","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1109\/TSE.2018.2870895","volume":"46","author":"V Nair","year":"2020","unstructured":"Nair, V., Yu, Z., Menzies, T., Siegmund, N., Apel, S.: Finding faster configurations using FLASH. IEEE Trans. Software Eng. 46(7), 794\u2013811 (2020)","journal-title":"IEEE Trans. Software Eng."},{"key":"5_CR38","unstructured":"Pavansubhasht: Ibm HR analytics employee attrition & performance (2025), kaggle"},{"key":"5_CR39","unstructured":"Rajarshi, K.A.: Life expectancy (who) dataset (2025), kaggle"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Rayegan, A., Menzies, T.: Minimal data, maximum clarity: a heuristic for explaining optimization. J. Syst. Softw., 112897 (2026)","DOI":"10.1016\/j.jss.2026.112897"},{"issue":"5","key":"5_CR41","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019). https:\/\/doi.org\/10.1038\/s42256-019-0048-x","journal-title":"Nat. Mach. Intell."},{"key":"5_CR42","doi-asserted-by":"publisher","first-page":"507","DOI":"10.2307\/2529204","volume":"30","author":"AJ Scott","year":"1974","unstructured":"Scott, A.J., Knott, M.: A cluster analysis method for grouping means in the analysis of variance. Biometrics 30, 507\u2013512 (1974)","journal-title":"Biometrics"},{"key":"5_CR43","unstructured":"Senthilkumar, L., Menzies, T.: Can large language models improve se active learning via warm-starts? ACM Trans. Soft. Eng. Methodol. (2024)"},{"issue":"1","key":"5_CR44","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s10664-024-10564-3","volume":"30","author":"L Sorokin","year":"2025","unstructured":"Sorokin, L., Safin, D., Nejati, S.: Can search-based testing with pareto optimization effectively cover failure-revealing test inputs? Empir. Softw. Eng. 30(1), 26 (2025). https:\/\/doi.org\/10.1007\/s10664-024-10564-3","journal-title":"Empir. Softw. Eng."},{"key":"5_CR45","unstructured":"Syedfaizanalii: Car price dataset - cleaned (2025), kaggle"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Tantithamthavorn, C.: Automated parameter optimization of classification techniques for defect prediction models. In: Proc. ICSE, pp. 321\u2013332. ACM (2016)","DOI":"10.1145\/2884781.2884857"},{"key":"5_CR47","unstructured":"Valerdi, R.: Heuristics for systems engineering cost estimation. IEEE Sys. J. (2010)"},{"key":"5_CR48","unstructured":"Zeleny, M.: Multiple Criteria Decision Making. McGraw-Hill, New York (1982)"},{"issue":"6","key":"5_CR49","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., Li, H.: MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712\u2013731 (2007)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"5_CR50","unstructured":"Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm, ETH Zurich (2001). 103 Tech. Rep. TIK-Report"}],"container-title":["Lecture Notes in Computer Science","Search-Based Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-30699-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:43:23Z","timestamp":1783575803000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-30699-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,10]]},"ISBN":["9783032306982","9783032306999"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-30699-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,10]]},"assertion":[{"value":"10 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"SSBSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Search Based Software Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ssbse2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.researchr.org\/home\/ssbse-2026","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}