Evolutionary Ensemble Search: Council-Guided Program Evolution with Persistent Memory
Evolutionary Ensemble Search (EES) constructs machine-learning procedures through expert-guided program evolution. A role-specialized council turns task evidence and experimental results into structured search directions. An orchestrator allocates these directions to execution specialists and an evolutionary engine. The engine selects measured parents, diagnoses their errors, and produces descendants through code mutation, structured pipeline edits, and crossover. Each child must execute and acquire its own validation evidence. Population archives retain useful alternatives, while compatible predictions compete in a validation-gated ensemble stage. Search adapts through parent-relative op...
| Authors | Juan P. Madrigal-Cianci, Eshan Chordia |
|---|---|
| Primary category | cs.NE |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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