A note on using performance and data profilesfor training algorithms

11/26/2017

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It is shown how to use the performance and data profile benchmarking tools to improve algorithms' performance. An illustration for the BFO derivative-free optimizer suggests that the obtained gains are potentially significant.

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A note on using performance and data profilesfor training algorithms

Statistical generalized derivative applied to the profile likelihood estimation in a mixture of semiparametric models

Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms

Training Learned Optimizers with Randomly Initialized Learned Optimizers

Carl-Hauser -- Open Source Image Matching Algorithms Benchmarking Framework

Coverage Improvement of Wireless Sensor Networks via Spatial Profile Information

Enhancing Optimization Performance: A Novel Hybridization of Gaussian Crunching Search and Powell's Method for Derivative-Free Optimization

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A note on using performance and data profilesfor training algorithms

Related Research

Statistical generalized derivative applied to the profile likelihood estimation in a mixture of semiparametric models

Benchmarking Processor Performance by Multi-Threaded Machine Learning Algorithms

Training Learned Optimizers with Randomly Initialized Learned Optimizers

Carl-Hauser -- Open Source Image Matching Algorithms Benchmarking Framework

Coverage Improvement of Wireless Sensor Networks via Spatial Profile Information

Enhancing Optimization Performance: A Novel Hybridization of Gaussian Crunching Search and Powell's Method for Derivative-Free Optimization

Adjusting Rate of Spread Factors through Derivative-Free Optimization: A New Methodology to Improve the Performance of Forest Fire Simulators