Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds | Zendy

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Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds

Author(s) -

Tom Schaul

Publication year - 2012

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/2330784.2330816

Subject(s) - benchmarking , computer science , exponential function , black box , class (philosophy) , separable space , mathematical optimization , noise (video) , artificial intelligence , algorithm , theoretical computer science , machine learning , mathematics , mathematical analysis , marketing , business , image (mathematics)

Natural Evolution Strategies (NES) are a recent member of the class of real-valued optimization algorithms that are based on adapting search distributions. Exponential NES (xNES) are the most common instantiation of NES, and particularly appropriate for the BBOB 2012 benchmarks, given that many are non-separable, and their relatively small problem dimensions. This report provides the the most extensive empirical results on that algorithm to date, on both the noise-free and noisy BBOB testbeds.

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