Stochastic optimization with adaptive restart: a framework for integrated local and global learning
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Summary
The stochastic optimization with adaptive restart (SOAR) framework is proposed, that uses the predictive capability of Gaussian process models as a means to adaptively restart local search and intelligently select restart locations with current information.
- Type
- article
- Published
- 2020-07-28
- Cited by
- 31
- References
- 63
- OpenAlex
- https://openalex.org/W3046086400
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:225509217
Keywords
Soar, Local optimum, Mathematical optimization, Local search (optimization), Global optimization
References
- Random Restarts in Global Optimization
- Stochastic Adaptive Search for Global Optimization
- Trust Region Methods
- Introduction to stochastic search and optimization - estimation, simulation, and control
- Handbook of Simulation Optimization
- Efficient Global Optimization of Expensive Black-Box Functions
- Handbook of global optimization
- A multistart gradient-based algorithm with surrogate model for global optimization
- A Note on the Griewank Test Function
- Bayesian Algorithms for One-Dimensional Global Optimization
- Handbook of Knowledge Representation
- On the Convergence of the P-Algorithm for One-Dimensional Global Optimization of Smooth Functions
- SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization
- A quasi-multistart framework for global optimization of expensive functions using response surface models
- Sequential stopping rules for the multistart algorithm in global optimisation
- Hybrid Genetic Algorithm—Local Search Methods for Solving Groundwater Source Identification Inverse Problems
- Improved Strategies for Radial basis Function Methods for Global Optimization
- Optimal and sub-optimal stopping rules for the Multistart algorithm in global optimization
- Nonlinear numerical optimization with use of a hybrid Genetic Algorithm incorporating the Modified Powell method
- Stochastic Trust-Region Response-Surface Method (STRONG) - A New Response-Surface Framework for Simulation Optimization
Cited by
- ARCH-COMP 2019 Category Report: Falsification
- Falsification of Cyber-Physical Systems with Robustness Uncertainty Quantification Through Stochastic optimization with Adaptive Restart
- Scalable Constrained Bayesian Optimization
- Multistart algorithm for identifying all optima of nonconvex stochastic functions
- (Global) Optimization: Historical notes and recent developments
- Efficient Optimization-Based Falsification of Cyber-Physical Systems with Multiple Conjunctive Requirements
- Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation With Probabilistic Guarantees
- CauseBox: A Causal Inference Toolbox for BenchmarkingTreatment Effect Estimators with Machine Learning Methods
- ARCH-COMP 2021 Category Report: Falsification with Validation of Results
- An Efficient Direct Search Method for Simulation Optimization With Conditional-Expectation- Based Objectives
- Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous Decisions
- Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design
- Multi-Start, Random Reselection of Algorithms or Both?
- Gaussian Processes for High-Dimensional, Large Data Sets: A Review
- Assessment of effective reactive power reserve in power system networks under uncertainty applying coronavirus herd immunity optimizer (CHIO) for operation simulation
- Search Based Testing for Code Coverage and Falsification in Cyber-Physical Systems
- Learning test generators for cyber-physical systems
- Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
- A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics
- Multi Agent Rollout for Bayesian Optimization
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