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:

Main changes:

• new VD-CMA algorithm with linear time and space complexity for black-box optimization

• API control of stopping criteria, with individual activation scheme

• improved memory control when tackling large-scale optimization problems

• solutions now support printing out in pheno space

• improved API of solutions object

• fixed compilation error with gcc 4.7

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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