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Random optimization

Known as: Random optimisation, Randomized optimization 
Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can… 
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Related topics

Related topics

12 relations
Artificial intelligenceEvolution strategyGradientHill climbing

Broader (1)

Mathematical optimization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2014
2014
Vehicle License Plate Recognition With Random Convolutional Networks
Despite decades of research on automatic license plate recognition (ALPR), optical character recognition (OCR) still leaves room… 
Highly Cited
2005
Highly Cited
2005
Chaos synchronization and parameter identification for gyroscope system
2005
2005
Optimal design of robust adaptive controllers a QFT-EEAS based approach using random optimization techniques
Quantitative design of robust control systems proposes a transparent and practical controller design methodology for uncertain… 
2005
2005
Analysis of multiplier with a novel symmetric ferroelectric varactor
This work is aimed at investigating the potential of (Ba,Sr)TiO3 ferroelectric varactors in frequency multipliers in the 100 GHz… 
2001
2001
Low-power CMOS with subvolt supply voltages
  • Mircea R. Stan
  • IEEE Transactions on Very Large Scale Integration…
  • 2001
  • Corpus ID: 16560032
We first present a circuit taxonomy along the space and time dimensions, which is useful for classifying generic low-power… 
2001
2001
Global random optimization by simultaneous perturbation stochastic approximation
A desire with iterative optimization techniques is that the algorithm reach the global optimum rather than get stranded at a… 
2000
2000
Stochastic approximation for global random optimization
A desire with iterative optimization techniques is that the algorithm reach the global optimum rather than get stranded at a… 
1999
1999
Fuzzy control of unknown multiple-input-multiple-output plants
1996
1996
On the Glivenko-Cantelli Problem in Stochastic Programming: Linear Recourse and Extensions
Integrals of optimal values of random linear programming problems depending on a finite dimensional parameter are approximated by… 
Highly Cited
1994
Highly Cited
1994
A hybrid algorithm for finding the global minimum of error function of neural networks and its applications
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