Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
2018 Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors
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Drift analysis on a mixed-integer benchmark shows (1+1)-LB-ES risks premature convergence with large numbers of integer variables while (1+1)-LUB-ES achieves linear convergence after integers are fixed under suitable bounds.
MONET represents tasks as graph nodes and uses neighbor-based crossover plus per-task mutation to transfer knowledge, matching or exceeding MAP-Elites performance on four large-scale simulation domains.
AC/DC coevolves LLMs via merging and tasks via synthetic generation to produce compact expert model archives with broader benchmark coverage than larger baselines.
citing papers explorer
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Towards symbolic regression for interpretable clinical decision scores
Brush is a new symbolic regression method that integrates tree-like rules with function optimization, matching or beating decision trees and forests on clinical scoring tasks while producing simpler interpretable models.
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Convergence Analysis of Evolution Strategies for Mixed-Integer Optimization
Drift analysis on a mixed-integer benchmark shows (1+1)-LB-ES risks premature convergence with large numbers of integer variables while (1+1)-LUB-ES achieves linear convergence after integers are fixed under suitable bounds.
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Multi-Task Optimization over Networks of Tasks
MONET represents tasks as graph nodes and uses neighbor-based crossover plus per-task mutation to transfer knowledge, matching or exceeding MAP-Elites performance on four large-scale simulation domains.
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Discovering Novel LLM Experts via Task-Capability Coevolution
AC/DC coevolves LLMs via merging and tasks via synthetic generation to produce compact expert model archives with broader benchmark coverage than larger baselines.