[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-327301-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-327301-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","evolutionary-feature-engineering-for-structured-data-read-online-free","Evolutionary Feature Engineering for Structured Data - read online free","","Large language models increasingly support open-ended evolutionary optimization for structured-data preprocessing. Evolutionary Feature Engineering (EFE) evolves LLM-generated Python programs that implement standardized fit/transform interfaces, enabling direct insertion into existing machine-learning pipelines. Evolution refines candidates using dataset context, summary statistics, and validation feedback from downstream task performance. 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It represents transformations as Python programs with a standardized fit/transform interface so they can be inserted into machine learning pipelines.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does EFE evaluate candidate transformations during evolution?",{"text":68,"@type":64},"EFE evaluates each candidate by inserting it before a fixed downstream model and returning validation performance as feedback to the evolutionary loop. Dataset metadata, summary statistics, and prior trial outcomes guide the LLM proposals.",{"name":70,"@type":61,"acceptedAnswer":71},"What are the main differences between EFE-Time and EFE-Tab?",{"text":72,"@type":64},"EFE-Time evolves invertible, dataset-specific normalization programs for time-series forecasting, improving foundation-model extrapolation. EFE-Tab evolves small, high-value feature programs for tabular prediction, aiming to improve validation AUC while adding or dropping features as needed.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},327301,1790094476,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":145,"read_time":146,"preview_page_count":4},1649267921044,"https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345","arXiv :2607 .0 1548v 1 [ cs .LG] 2 Jul 2026  \nEvolutionary Feature Engineering for Structured Data  \nEge Onur Taga 1 Yilin Zhuang 1 M. Emrullah Ildiz 1 Petros Mol2 Abhimanyu Das2 Karthik Duraisamy 1 Samet Oymak 1,2  \n1 University of Michigan 2 Google Research  \nAbstract  \nLarge language models are increasingly used as open-ended search operators in evolutionary optimization. We introduce Evolutionary Feature Engineering (EFE), a framework for using LLM-based evolution to discover preprocessing transformations for structured data. EFE represents transformations as Python programs with a standardized fit/transform interface, allowing them to be inserted directly into existing machine learning pipelines. During evolution, candidate programs are refined using dataset context, summary statistics, and downstream performance feedback on validation set. We instantiate EFE in two settings. For time-series forecasting, EFE-Time learns invertible, dataset-specific normalizations that improve off-the-shelf time-series foundation models. It reduces forecasting errors (MASE, WQL, MAE) 3% or more when averaged across datasets and improvements areas much as 19% on the COVID-Deaths dataset. Notably, these improvements occur with recent TSFMs such as Chronos-2 . For tabular prediction, EFE-Tab evolves compact feature programs that add useful interpretable features and remove redundant ones, improving or matching existing LLM-based feature-engineering methods. We found EFE-Tab to be particularly effective on classical decision trees, where small sets of evolved features yield competitive accuracy while preserving interpretability. Overall, EFE demonstrates that LLM-based evolution can improve both accuracy and interpretability when automatically tackling structured data.  \n1 Introduction  \nFeature engineering has a simple premise: a model need not change if the data can be presented in abetter form. In forecasting, normalization or detrending can make a sequence easier to extrapolate; in tabular prediction, a ratio, interaction, threshold, or aggregate can make a simple classifier substantially more expressive. Such transformations are often obvious in hindsight to human experts but difficult to specify in advance, since they depend on the dataset, the downstream model, and structure not captured by fixed preprocessing libraries.  \nLarge language models make it possible to search over a richer open-ended space of executable feature-engineering programs. Recent systems such as AlphaEvolve (Novikov et al., 2025) show that LLMs can guide evolutionary code discovery, while LLM-based feature-engineering methods such as CAAFE and LLM-FE show that dataset context can help construct useful tabular features (Hollmannet al., 2023 ; Abhyankar et al., 2025) . These results suggest a broader question: can LLM-based evolution discover useful preprocessing programs for structured data, specifically time series and tables, where temporal order and column structure constrain and guide LLM’s action space?  \nWe propose Evolutionary Feature Engineering (EFE) as a framework for evolving feature transformations as Python programs. Each candidate follows a standard fit/transform interface and is evaluated by inserting it before a fixed downstream model. An LLM proposes transformations using dataset metadata, summary statistics, and outcomes of previous trials; validation performance is then returned to the evolutionary loop as feedback. Thus, search is driven directly by downstream task performance and dataset-specific context which captures domain expertise.  \nCorrespondence to \u003C[egetaga@umich.edu](egetaga@umich.edu)>.  \n\n| | feedback | \u003Cbr>EFE-Time |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n|  |  |  | Original series\u003Cbr>History Truth Forecast | Transformed space\u003Cbr>History Truth Forecast | Inverse transform\u003Cbr>History Truth Forecast |\n|  |  |  | \u003Cbr>|  |  |\n|  |  |  | EFE-Tab |  |  |\n|  |  | |  |  |  |\n|  |  | |  |  |  |\n\nFigure 1: Given a dataset and optional","cbCaiudptdZTqha5","https://ap.wps.com/l/cbCaiudptdZTqha5","pdf",1849926,42,"English","# Introduction\n## Evolutionary Feature Engineering (EFE)\n## EFE-Time for time-series forecasting\n## EFE-Tab for tabular prediction","[{\"question\":\"What is Evolutionary Feature Engineering (EFE)?\",\"answer\":\"EFE is a framework that uses LLM-based evolution to discover preprocessing transformations for structured data. It represents transformations as Python programs with a standardized fit/transform interface so they can be inserted into machine learning pipelines.\"},{\"question\":\"How does EFE evaluate candidate transformations during evolution?\",\"answer\":\"EFE evaluates each candidate by inserting it before a fixed downstream model and returning validation performance as feedback to the evolutionary loop. Dataset metadata, summary statistics, and prior trial outcomes guide the LLM proposals.\"},{\"question\":\"What are the main differences between EFE-Time and EFE-Tab?\",\"answer\":\"EFE-Time evolves invertible, dataset-specific normalization programs for time-series forecasting, improving foundation-model extrapolation. EFE-Tab evolves small, high-value feature programs for tabular prediction, aiming to improve validation AUC while adding or dropping features as needed.\"}]","Evolutionary Feature Engineering for Structured Data - read online free | PDF",1789976944,106]