Crab Research
Machine learning

Cross-Dimensional Covariance Adaptation Induces Irreversible Drift Toward Deceptive Attractors in Barrier-Constrained Search

Li, Alex Chengyu

Working Paper · ZenodoFirst public

Overview

We report covariance-induced irreversible drift, a failure mode of CMA-ES in barrier-constrained optimization.

Original abstract (English)

We report covariance-induced irreversible drift, a failure mode of CMA-ES in barrier-constrained optimization. Full CMA-ES systematically transitions from feasible to infeasible solutions through cross-dimensional covariance adaptation (73% drift rate vs 43% for sep-CMA-ES, Fisher p=0.018). The underlying landscape contains empirically disconnected feasible basins linked by directed, optimizer-dependent transitions. Population scaling experiments show complete separation (100% vs 0%) at lambda=200. Adaptive mitigation via reactive switching fails; preemptive variant selection is required.

Public abstract source

Computer ScienceMachine learningCMA-ESconstrained optimizationdeceptive attractorsevolution strategiesirreversible driftbasin topologydirected transition graphblack-box optimization
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