SMAC: Sequential Model-based Algorithm Configuration

Bioinformatics, and Empirical & Theoretical Algorithmics Laboratory (ß-Lab)
Department of Computer Science
The University of British Columbia

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July 19th, 2015 New release (version 2.10.03) which has a minor bugfix.
July 5th, 2015 New release (version 2.10.02) which has a new PCS format, and other bug fixes. License is now AGPLv3.
August 4th, 2014 New release (version 2.08.00) which should be much simpler to use
October, 2013 New release (version 2.06.01) contains some minor bug fixes
August 28th, 2013 New release (version 2.06.00) which contains a bunch of bug fixes and usability improvements
August 7th, 2013 New release (version 2.04.02) which contains a pair of bug fixes. Additionally a beta release (2.06.00b) has also been posted which has some new utilities and more bug fixes.
February 16, 2013 New release (version 2.04.01), including some bug fixes, usability improvements and  minor feature improvements
October 25th, 2012 After a series of internal releases, SMAC version 2 has been publically released! In short, this is a complete rewrite
of SMAC in Java that features many  improvements, is well documented, and is portable & easy to use.
February 1, 2012 A substantially improved version of SMAC will be available soon; if you want to start using SMAC in the meantime, please send a quick email to Frank.
We also plan to provide a quickstart guide similar to the one for ParamILS, as well as a Java implementation of SMAC.
September 9, 2011 First version of this page set up. Before this, SMAC was only available upon request.                                                                                            
  More News...


SMAC (sequential model-based algorithm configuration) is a versatile tool for optimizing algorithm parameters (or the parameters of some other process we can run automatically, or a function we can evaluate, such as a simulation).
SMAC has helped us speed up both local search and tree search algorithms by orders of magnitude on certain instance distributions. Recently, we have also found it to be very effective for the hyperparameter optimization of machine learning algorithms, scaling better to high dimensions and discrete input dimensions than other algorithms. Finally, the predictive models SMAC is based on can also capture and exploit important information about the model domain, such as which input variables are most important.
We hope you find SMAC similarly useful. Ultimately, we hope that it  helps algorithm designers focus on tasks that are more scientifically valuable than parameter tuning.


Previous Versions



SMAC is licensed under the AGPLv3. Please contact Frank Hutter to discuss other licensing options.
Note:SMAC versions before v2.10.02 are not licensed under AGPLv3 but are free for academic & non-commercial usage.


For any comments, questions or concerns please check out the SMAC forum available here




We maintain algorithm configuration benchmarks on the Automated Algorithm Configuration project page.