[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82152-en":3,"doc-seo-82152-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82152,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Evolutionary Intelligence for Scientific Discovery","Artificial intelligence is shifting scientific discovery from task-specific workflows to autonomous systems that explore open-ended candidate spaces using experimental and human feedback. Evolutionary computation supports feedback-driven discovery by maintaining diverse candidates and steering search through accumulated evidence. Traditional EC mainly refines candidates for predefined problems, while cumulative discovery also requires retaining experience. The review proposes evolutionary intelligence (EI), a five-dimensional framework for linking candidate evolution with experience retention across cycles, enabling cumulative scientific insight.","arXiv :2607 .09025v 1 [ cs .NE] 10 Jul 2026  \nEvolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems  \nChao Wang 1 , Lingling Li 1 , Fang Liu 1 , Licheng Jiao 1*  \n1* School of Artificial Intelligence, Xidian University, No. 2 South Taibai  \nRoad, Xi’an, 710071, Shaanxi, China.  \n*Corresponding author(s). E-mail(s): [lchjiao@mail.xidian.edu.cn](lchjiao@mail.xidian.edu.cn) ;  \nContributing authors: [xiaofengxd@126.com](xiaofengxd@126.com) ; [llli@xidian.edu.cn](llli@xidian.edu.cn) ;  \n[f63liu@163.com](f63liu@163.com) ;  \nAbstract  \nArtificial 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. 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. EI characterizes scientific AI systems that sustain exploration by linking candidate refinement with experience retention across evolutionary cycles. We introduce a five-dimensional analytical framework that asks what evolves, how candidates change, why candidates are selected, where feedback originates, and when evolution occurs. This framework clarifies how EI transforms isolated search trajectories into cumulative scientific insight. We further demonstrate this paradigm across diverse discovery modes, from evolving concrete scientific entities to orchestrating automated research workflows. Finally, we identify critical bottlenecks regarding evaluation, process traceability, and shared infrastructure, providing a concrete roadmap for advancing the transition from EC to EI in scientific discovery.  \nKeywords: Evolutionary computation, AI for Science, scientific discovery,  \nautonomous experimentation, foundation models, self-evolving agents  \n1  \n1 Introduction  \nArtificial intelligence (AI) increasingly serves as a general-purpose tool for scientific discovery, transitioning from task-specific automation to systems that organize scientific exploration [1, 2] . By leveraging experimental feedback and human interaction, AI systems can explore scientific problems through discovery cycles. This shift is evident across disciplines, where AI actively investigates candidate spaces in molecular and materials design [3, 4], protein engineering [5], and automated laboratories [6, 7], moving beyond passive data analysis.  \nFeedback-driven discovery explores open-ended candidate spaces where boundaries are often undefined and emerge as evidence accumulates [1, 8] . Because experimental feedback is costly and physically constrained, every evaluated candidate, including failures, constitutes valuable scientific evidence [9, 10] . Consequently, the evaluation history can reveal hidden structures within the candidate space and support diverse exploratory behaviours [8, 11] .  \nEvolutionary computation (EC) provides a foundational computational framework to support these discovery cycles [12, 13] . Rather than relying on a single-point search, EC maintains populations of scientific candidates that are iteratively updated by evolutionary operators according to feedback. This population-based approach is particularly well suited to open-ended candidate spaces characterized by weak or absent gradients, noisy feedback, and dynamically evolving boundaries [8, 14–16] . By steering exploration toward regions supported by accumulated evidence, EC sustainsthe creation of diverse scientific candidates across discovery cycles. This approach encompasses various common algorithms, such as classical genet","cbCaihPTsl0Amonr","https://ap.wps.com/l/cbCaihPTsl0Amonr","pdf",1031472,2,1,19,"English","en",105,"# Abstract\n# Introduction\n## AI for scientific discovery and discovery cycles\n## Feedback-driven discovery in open-ended candidate spaces\n## Evolutionary computation as a foundation for discovery cycles\n## From candidate refinement to cumulative discovery\n## Evolutionary intelligence and a five-dimensional framework\n# Future directions and bottlenecks","[{\"question\":\"What problem does the review address in evolutionary computation for scientific discovery?\",\"answer\":\"Conventional evolutionary computation often focuses on refining candidates for predefined problems, but cumulative discovery also needs experience retention across iterations.\"},{\"question\":\"How does evolutionary intelligence (EI) differ from single-task evolutionary search?\",\"answer\":\"EI links candidate refinement with structured experience retention, reusing not only successful candidates but also failed trials and candidate lineages as scientific evidence.\"},{\"question\":\"What are the main bottlenecks for moving from EC to EI identified by the review?\",\"answer\":\"Critical bottlenecks include evaluation challenges, process traceability, and the need for shared infrastructure to support cumulative, reusable discovery.\"}]",1784178474,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evolutionary-intelligence-for-scientific-discovery","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/evolutionary-intelligence-for-scientific-discovery/82152/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-20","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What 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