CMA-ES
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Related Articles from SNS
Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Announce Type: new Abstract: Covariance matrix adaptation evolution strategy (CMA-ES) is a state-of-the-art black-box optimization algorithm. In general, CMA-ES uses a portfolio of multiple stopping criteria to automatically determine when to stop the search. This mechanism aims to avoid unnecessary consumption of the function evaluation budget during stagnation.
BERS: Locally Optimal Continuous Algorithm for Maritime Weather Routing with Just-in-Time Arrival
new Abstract: Maritime weather routing must optimize route geometry under dynamic wind-wave conditions, obstacle constraints, and fixed-arrival requirements. We present B\'ezier Evolve and Refine Strategy (\name{}), a two-stage framework that combines global evolutionary search (CMA-ES) with local variational refinement (FMS). Routes are parametrized as B\'ezier curves and evaluated with dense along-path sampling, enabling smooth trajectories while preserving practical feasibility...
Can LLMs Beat Classical Hyperparameter Optimization Algorithms?
Computer Science > Machine Learning [Submitted on 25 Mar 2026 (v1), last revised 17 Apr 2026 (this version, v5)] Title:Can LLMs Beat Classical Hyperparameter Optimization Algorithms?