Project details for libcmaes

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Version:
0.9.3
Submitter:
beniz
Author(s):
Emmanuel Benazera
Posted:
17 November 2014
Last Updated:
17 November 2014
Open Source License:
lgpl version 3 or later
Programming Language(s):
c++,python

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Logo libcmaes 0.9.3

by beniz - November 17, 2014, 14:04:10 CET [ Project Homepage BibTeX Download ]

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Description:

libcmaes is a multithreaded C++ (with Python bindings) implementation of the CMA-ES algorithm for stochastic optimization of nonlinear 'blackbox' functions. The implemented algorithms have a wide range of applications in various disciplines, ranging from pure function optimization, optimization in industrial and scientific applications, to the solving of reinforcement and machine learning problems.

Current features include: high-level API for simple use in external applications, implementatio of several flavors of CMA-ES, IPOP-CMA-ES, BIPOP-CMA-ES, active CMA-ES, active IPOP and BIPOP restart strategies, sep-CMA-ES and VD-CMA (linear time & space complexity) along with support for IPOP and BIPOP flavors as well.

Some operations benefit from multicores, and there's support for objective function gradient, when available. A control exe in the command line is provided for running the algorithm over a range of classical single-objective optimization problems.

Full documentation is available from https://github.com/beniz/libcmaes/wiki

Developer API documentation is available from http://beniz.github.io/libcmaes/doc/html/index.html

Changes to previous version:

This is an important update:

  • full support for surrogates, allowing optimization of costly objective functions, ref #57

  • integrated rankign SVM default surrogate, ref #83

  • Python bindings for surrogates, ref #75

  • more informed optimization status and error messages, ref #85

  • API for computing confidence intervals around optima, ref #30

  • API for computing 2D contour around optima, ref #31

  • new 'elitist' scheme for improved restart strategy useful on some rather difficult functions, ref #77

  • fixed Eigen namespace import, ref #62

  • fixed and added new parameter vector getter in Candidate, ref #84

BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Mac Os X
Data Formats: Any
Tags: Black Box Optimization, Evolution Strategy, Stochastic Optimization
Archive: download here

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