An adaptive target-variance scheme for Monte Carlo integration inside sEGO improves performance over fixed-variance and multi-start baselines on stochastic benchmarks and a tuned-mass-damper problem while enabling high stochastic dimension counts.
Globalized nelder--mead method for engineering optimization
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Monte Carlo Integration with adaptive variance selection for improved stochastic Efficient Global Optimization
An adaptive target-variance scheme for Monte Carlo integration inside sEGO improves performance over fixed-variance and multi-start baselines on stochastic benchmarks and a tuned-mass-damper problem while enabling high stochastic dimension counts.