The Canonical Evolutionary Strategy converges globally by spectral concentration on the principal eigenfunction of a replicator-mutator operator, explaining survival of the flattest.
A class of smooth exact penalty function methods for optimization problems 43 with orthogonality constraints
7 Pith papers cite this work. Polarity classification is still indexing.
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Superposition relaxation creates separable estimators for factorable functions that are tighter than McCormick relaxations in numerical tests while providing convergence guarantees.
MSC-CMA-ES makes CMA-ES restarts structure-aware via cyclic nearest-better basin discovery on Sobol pre-samples, achieving 2.7x higher target coverage than BIPOP-CMA-ES on composition functions across CEC suites.
A general framework for parameter-free smooth nonconvex optimization via higher-order regularization yields algorithms with optimal complexity bounds without prior parameter knowledge.
Active party in VFL performs agnostic inference attacks via independent models on logistic regression and counters them with tunable distortion of passive-party parameters.
Genetic algorithm optimizes parameters of multi-agent flocking models to match user-defined objectives, with alignment emerging from spacing maintenance.
Empirical benchmarking shows tolfunhist and the full portfolio stop CMA-ES closest to the optimal evaluation count on BBOB, while tolfun and tolfunhist often trigger before full stagnation.
citing papers explorer
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From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy
The Canonical Evolutionary Strategy converges globally by spectral concentration on the principal eigenfunction of a replicator-mutator operator, explaining survival of the flattest.
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Relaxation via Separable Estimators: Arithmetic and Implementation
Superposition relaxation creates separable estimators for factorable functions that are tighter than McCormick relaxations in numerical tests while providing convergence guarantees.
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MSC-CMA-ES: Structure-Aware Restarts for CMA-ES via Cyclic Nearest-Better Basin Discovery
MSC-CMA-ES makes CMA-ES restarts structure-aware via cyclic nearest-better basin discovery on Sobol pre-samples, achieving 2.7x higher target coverage than BIPOP-CMA-ES on composition functions across CEC suites.
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A General Recipe for Parameter-Free Nonconvex Optimization via Higher-Order Regularization
A general framework for parameter-free smooth nonconvex optimization via higher-order regularization yields algorithms with optimal complexity bounds without prior parameter knowledge.
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Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
Active party in VFL performs agnostic inference attacks via independent models on logistic regression and counters them with tunable distortion of passive-party parameters.
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EvoFlock: evolved inverse design of multi-agent motion
Genetic algorithm optimizes parameters of multi-agent flocking models to match user-defined objectives, with alignment emerging from spacing maintenance.
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Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Empirical benchmarking shows tolfunhist and the full portfolio stop CMA-ES closest to the optimal evaluation count on BBOB, while tolfun and tolfunhist often trigger before full stagnation.