Description:

libcmaes is a multithreaded C++11 (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 a major release, with several novelties, improvements and fixes, among which:

• step-size two-point adaptaion scheme for improved performances in some settings, ref #88

• important bug fixes to the ACM surrogate scheme, ref #57, #106

• simple high-level workflow under Python, ref #116

• improved performances in high dimensions, ref #97

• improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

• elitist mechanism for forcing best solutions during evolution, ref 103

• new legacy plotting function, ref #110

• optional initial function value, ref #100

• improved C++ API, ref #89

• Python bindings support with Anaconda, ref #111

• configure script now tries to detect numpy when building Python bindings, ref #113

• Python bindings now have embedded documentation, ref #114

• support for Travis continuous integration, ref #122

• lower resolution random seed initialization

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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