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IEEE Congr. Evol. Comput.","author":"Tao"},{"key":"ref136","article-title":"Large language models for software engineering: Survey and open problems","author":"Fan","year":"2023","journal-title":"arXiv:2310.03533"},{"key":"ref137","article-title":"CoCo: Testing code generation systems via concretized instructions","author":"Yan","year":"2023","journal-title":"arXiv:2308.13319"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1109\/QSIC.2011.19"},{"key":"ref139","first-page":"416","article-title":"EvoSuite: Automatic test suite generation for object-oriented software","volume-title":"Proc. 19th ACM SIGSOFT Symp. 13th Eur. Conf. Found. Softw. Eng.","author":"Fraser"},{"key":"ref140","first-page":"1","article-title":"InCoder: A generative model for code infilling and synthesis","volume-title":"Proc. 11th Int. Conf. Learn. Rep.","author":"Fried"},{"issue":"2","key":"ref141","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TSE.2017.2663435","article-title":"Automated test case generation as a many-objective optimization problem with dynamic selection of the targets","volume":"44","author":"Panichella","year":"2018","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref142","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s10515-016-0197-7","article-title":"Analysing the fitness landscape of search-based software testing problems","volume":"24","author":"Aleti","year":"2017","journal-title":"Autom. Softw. Eng."},{"key":"ref143","first-page":"1204","article-title":"Causes and effects of fitness landscapes in unit test generation","volume-title":"Proc. Genet. Evol. Comput. Conf.","author":"Albunian"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3228739"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2016.00040"},{"key":"ref146","first-page":"3","article-title":"CodeGen: An open large language model for code with multiturn program synthesis","volume-title":"Proc. 11th Int. Conf. Learn. Rep.","author":"Nijkamp"},{"key":"ref147","first-page":"1","article-title":"SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization","volume-title":"Proc. Evol. Methods Design Optim. Control Appl. Ind. Problems (EUROGEN)","author":"Zitzler"},{"key":"ref148","article-title":"SCAPE: Searching conceptual architecture prompts using evolution","author":"Lim","year":"2024","journal-title":"arXiv:2402.00089"},{"key":"ref149","first-page":"179","article-title":"Prompt-guided level generation","volume-title":"Proc. Companion Conf. Genet. Evol. Comput.","author":"Sudhakaran"},{"key":"ref150","first-page":"1","article-title":"The VGLC: The video game level corpus","volume-title":"Proc. 7th Workshop Procedural Content Gener.","author":"Summerville"},{"key":"ref151","first-page":"329","article-title":"Exploiting open-endedness to solve problems through the search for novelty","volume-title":"Proc. 11th Int. Conf. Synth. Simulat. Living Syst.","author":"Lehman"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1145\/3592116"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-48796-5_10"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1039\/d3dd00113j"},{"issue":"1","key":"ref155","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref156","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae675","volume-title":"Integrating genetic algorithms and language models for enhanced enzyme design","author":"Teukam","year":"2024"},{"issue":"1","key":"ref157","first-page":"5989","article-title":"An evolutionary model of personality traits related to cooperative behavior using a large language model","volume-title":"Sci. Rep.","volume":"14","author":"Suzuki","year":"2024"},{"key":"ref158","first-page":"3543","article-title":"Attention is not explanation","volume-title":"Proc. Conf. North Amer. Assoc. Comput. Linguist. Human Lang. Technol.","author":"Jain"},{"key":"ref159","article-title":"Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning","author":"Buehler","year":"2024","journal-title":"arXiv:2403.11996"},{"issue":"2","key":"ref160","article-title":"X-LoRA: Mixture of low-rank adapter experts, a flexible framework for large language models with applications in protein mechanics and molecular design","volume":"2","author":"Buehler","year":"2024","journal-title":"APL Mach. Learn."},{"key":"ref161","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1039\/D4DD00013G","article-title":"ProtAgents: Protein discovery via large language model multiagent collaborations combining physics and machine learning","volume":"3","author":"Ghafarollahi","year":"2024","journal-title":"Digit. Disc."},{"key":"ref162","doi-asserted-by":"crossref","DOI":"10.1016\/j.eml.2024.102131","article-title":"MechAgents: Large language model multiagent collaborations can solve mechanics problems, generate new data, and integrate knowledge","volume":"67","author":"Ni","year":"2024","journal-title":"Extreme Mech. Lett."},{"key":"ref163","article-title":"Investigate-consolidate-exploit: A general strategy for intertask agent self-evolution","author":"Qian","year":"2024","journal-title":"arXiv:2401.13996"},{"key":"ref164","article-title":"Agent-pro: Learning to evolve via policy-level reflection and optimization","author":"Zhang","year":"2024","journal-title":"arXiv:2402.17574"}],"container-title":["IEEE Transactions on Evolutionary Computation"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/4235\/10947080\/10767756.pdf?arnumber=10767756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:15:37Z","timestamp":1743639337000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10767756\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":164,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tevc.2024.3506731","relation":{},"ISSN":["1089-778X","1089-778X","1941-0026"],"issn-type":[{"value":"1089-778X","type":"print"},{"value":"1089-778X","type":"print"},{"value":"1941-0026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}