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Computational Statistics

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Computational Statistics

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Research Frontiers in Gradient-Free Optimization in Complex Landscapes

Design and analysis of optimization algorithms that operate without gradient information for non-differentiable, multimodal, or black-box objective functions.

Evolutionary Algorithms in High-Dimensional Rugged Terrain

Bayesian Optimization Beyond Gaussian Process Assumptions

Swarm Intelligence and Collective Search in Multimodal Spaces

Zeroth-Order Methods for Non-Convex Landscape Navigation

Black-Box Optimization Under Noisy Function Evaluation

Adaptive Sampling Strategies in Discontinuous Objective Functions

Metaheuristic Escapes from Deceptive Local Optima

Information-Theoretic Limits of Gradient-Free Search

Genetic Algorithms in Combinatorial and Mixed Search Spaces

Surrogate-Assisted Exploration of Expensive Black-Box Functions

All Computational Statistics PhD categories