Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems
Evolutionary computation (EC) provides a computational basis for feedback-driven discovery because population-based search can maintain diverse scientific candidates while steering exploration through accumulated evidence. However, EC predominantly focuses on candidate refinement for predefined problems, whereas cumulative discovery requires experience retention. To bridge this gap, this review introduces evolutionary intelligence (EI) for scientific discovery.
- ▪Evolutionary computation (EC) provides a computational basis for feedback-driven discovery because population-based search can maintain diverse scientific candidates while steering exploration through accumulated evidence.
- ▪However, EC predominantly focuses on candidate refinement for predefined problems, whereas cumulative discovery requires experience retention.
- ▪To bridge this gap, this review introduces evolutionary intelligence (EI) for scientific discovery.
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Computer Science > Neural and Evolutionary Computing arXiv:2607.09025 (cs) [Submitted on 10 Jul 2026] Title:Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems Authors:Chao Wang, Lingling Li, Fang Liu, Licheng Jiao View a PDF of the paper titled Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems, by Chao Wang and Lingling Li and Fang Liu and Licheng Jiao View PDF HTML (experimental) Abstract:Artificial intelligence (AI) is shifting scientific discovery from task-specific workflows towards autonomous systems that organize exploration with experimental and human feedback in open-ended candidate spaces.
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