A hybrid switching approach integrates Direct Search into model-based derivative-free optimization, with a convergence proof for single-objective cases and empirical gains on ML tasks and CUTEr benchmarks.
A fast and elitist multiobjective genetic algorithm: Nsga-ii.IEEE transactions on evolutionary computation, 6(2):182–197, 2002
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Iterative joint learning-optimization framework with convergent algorithms for pseudoconvex objectives in operational decision systems.
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Enhancing Model Based Derivative Free Optimization using Direct Search
A hybrid switching approach integrates Direct Search into model-based derivative-free optimization, with a convergence proof for single-objective cases and empirical gains on ML tasks and CUTEr benchmarks.
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Pseudoconvex Problems in Operational Decision Systems: Algorithms for Joint Learning and Optimization
Iterative joint learning-optimization framework with convergent algorithms for pseudoconvex objectives in operational decision systems.