Evolutionary Feature Engineering for Structured Data

arXiv cs.LG4w4 min read

arXiv:2607.01548v1 Announce Type: new Abstract: Large 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 perform

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Evolutionary Feature Engineering for Structured Data

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