VEGA is a derivative-free genetic algorithm that constructs Voronoi neighborhoods around retained elite candidates to balance exploitation and exploration in high-dimensional statistical optimization.
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Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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Voronoi-Elitism Genetic Algorithm: A Generic Derivative-Free Routine With Theory and Implementation for Statistical Optimization
VEGA is a derivative-free genetic algorithm that constructs Voronoi neighborhoods around retained elite candidates to balance exploitation and exploration in high-dimensional statistical optimization.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.