EvoForest evolves reusable computational graphs with callable transformations and low-dimensional trainable parts, reaching 94.13% ROC-AUC on the 2025 ADIA Lab Structural Break Challenge and beating the prior reported best of 90.14%.
Each output alternative is an expert in the ensemble; intermediate and callable nodes define alternative ways those experts are built and combined
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EvoForest: A Novel Machine-Learning Paradigm via Open-Ended Evolution of Computational Graphs
EvoForest evolves reusable computational graphs with callable transformations and low-dimensional trainable parts, reaching 94.13% ROC-AUC on the 2025 ADIA Lab Structural Break Challenge and beating the prior reported best of 90.14%.