Methods to compare expensive stochastic optimization algorithms with random restarts
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Publication:1756805
DOI10.1007/s10898-018-0673-7zbMath1411.90239OpenAlexW2808701200MaRDI QIDQ1756805
Shangwei Xie, Jason L. Loeppky, Warren L. Hare
Publication date: 27 December 2018
Published in: Journal of Global Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10898-018-0673-7
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Cites Work
- Unnamed Item
- Unnamed Item
- NOMAD
- Optimizing horizontal alignment of roads in a specified corridor
- Some asymptotic theory for the bootstrap
- Efficient optimization of the likelihood function in Gaussian process modelling
- Best practices for comparing optimization algorithms
- Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems
- Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms
- Optimization of Convex Functions with Random Pursuit
- Efficient Multi-Start Strategies for Local Search Algorithms
- A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set
- Introduction to Stochastic Search and Optimization
- Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics
- Benchmarking Derivative-Free Optimization Algorithms
- Mesh Adaptive Direct Search Algorithms for Constrained Optimization
- Benchmarking optimization software with performance profiles.
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