Hi Chaz, Thanks, for handling continuous parameters the approach we have in mind is to sample continuous values but then do some adaptive binning to reason about specific combinations of them – there are some details to be worked out though.
Simon From: "Chaz G." <chaz.gwen...@gmail.com> Date: Monday, 14 January 2019 at 19:20 To: "computer-go@computer-go.org" <computer-go@computer-go.org> Cc: Simon Lucas <simon.lu...@qmul.ac.uk> Subject: Re: [Computer-go] Efficient Parameter Tuning Software Hi Simon, Thanks for sharing. In my opinion, apart from discretizing the search space, the N-Tuple system takes a very intuitive approach to hyper-parameter optimization. The github repo readme notes you're working on an extended version to handle continuous parameters, what's your general approach to that issue? Thanks, -Chaz On Sun, Jan 13, 2019 at 11:51 AM Simon Lucas <simon.lu...@qmul.ac.uk<mailto:simon.lu...@qmul.ac.uk>> wrote: Hi all, The N-Tuple Bandit Evolutionary Algorithm aims to provide sample-efficient optimisation, especially for noisy problems. Software available in Java and Python: https://github.com/SimonLucas/ntbea It also provides stats on the value of each parameter setting and combinations of settings. Best wishes, Simon -- Simon Lucas Professor of Artificial Intelligence Head of School Electronic Engineering and Computer Science Queen Mary University of London _______________________________________________ Computer-go mailing list Computer-go@computer-go.org<mailto:Computer-go@computer-go.org> http://computer-go.org/mailman/listinfo/computer-go
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