pycma

[BibTeX] cite as:

Nikolaus Hansen, Youhei Akimoto, and Petr Baudis. CMA-ES/pycma on Github. Zenodo, DOI:10.5281/zenodo.2559634, February 2019.

pycma is a Python implementation of CMA-ES and a few related numerical optimization tools.

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a stochastic derivative-free numerical optimization algorithm for difficult (non-convex, ill-conditioned, multi-modal, rugged, noisy) optimization problems in continuous search spaces.

Useful links:

Installation of the latest release

In a system shell, type

    python -m pip install cma

to install the latest release from the Python Package Index (PyPI). The release link also provides more installation hints and a quick start guide.

    conda install --channel cma-es cma

installs from the conda cloud channel cma-es. CAVEAT: this distribution is currently not updated!

Installation from Github

The quick way (this requires git to be installed): to install the code from, for example, the master branch, copy-paste

    pip install git+https://github.com/CMA-ES/pycma.git@master

The long way:

get the package
- either download and unzip the code by clicking the green button above
- or, with git installed, type git clone https://github.com/CMA-ES/pycma.git
"install" the package
- either copy (or move) the cma source code folder into a folder which is in the Python path (e.g. the current folder)
- or modify the Python path to point to the folder where the cma package folder can be found. In both cases, import cma works without any further installation.
- or install the cma package by typing
```
    pip install -e cma
```
  in the folder where the cma package folder can be found. Moving the cma folder away from its location invalidates this installation.

It may be necessary to replace pip with python -m pip and/or prefixing either of these with sudo.

Version History

Release 4.0.0
- majorly improved mixed-integer handling based on a more concise lower bound of variances and on so-called integer centering
- moved options and parameters code into a new file
- many small-ish fixes and improvements
Release 3.4.0
- fix compatibility to numpy 2.0 (thanks to Sait Cakmak)
- improved interface to noise_handler argument which accepts True as value
- improved interface to ScaleCoordinates now also with lower and upper value mapping to [0, 1], see issue #210
- changed: 'ftarget' triggers with <= instead of <
- assign surrogate attribute (for the record) when calling fmin_lq_surr
- various (minor) bug fixes
- various (small) improvements of the plots and their usability
  - display iterations, evaluations and population size and termination criteria in the plots
  - subtract any recorded x from the plotted x-values by x_opt=index
- plots are now versus iteration number instead of evaluations by default
- provide legacy bbobbenchmarks without downloading
- new: CMADataLogger.zip allows sharing plotting data more easily by a zip file
- new: tolxstagnation termination condition for when the incumbent seems stuck
- new: collect restart terminations in cma.evalution_strategy.all_stoppings
- new: stall_sigma_change_on_divergence_iterations option to stall sigma change when the median fitness is worsening
- new: limit active C update for integer variables
- new: provide a COCO single function
Release 3.3.0 implements
- diagonal acceleration via diagonal decoding (option CMA_diagonal_decoding, by default still off).
- fmin_lq_surr2 for running the surrogate assisted lq-CMA-ES.
- optimization_tools.ShowInFolder to facilitate rapid experimentation.
- verb_disp_overwrite option starts to overwrite the last line of the display output instead of continuing adding lines to avoid screen flooding with longish runs (off by default).
- various smallish improvements, bug fixes and additional features and functions.
Release 3.2.2 fixes some smallish interface and logging bugs in ConstrainedFitnessAL and a bug when printing a warning. Polishing mainly in the plotting functions. Added a notebook for how to use constraints.
Release 3.2.1 fixes plot of principal axes which were shown squared by mistake in version 3.2.0.
Release 3.2.0 provides a new interface for constrained optimization ConstrainedFitnessAL and fmin_con2 and many other minor fixes and improvements.
Release 3.1.0 fixes the return value of fmin_con, improves its usability and provides a best_feasible attribute in CMAEvolutionStrategy, in addition to various other more minor code fixes and improvements.
Release 3.0.3 provides parallelization with OOOptimizer.optimize(..., n_jobs=...) (fix for 3.0.1/2) and improved pickle support.
Release 3.0.0 provides non-linear constraints handling, improved plotting and termination options and better resilience to injecting bad solutions, and further various fixes.
Version 2.7.1 allows for a list of termination callbacks and a light copy of CMAEvolutionStrategy instances.
Release 2.7.0 logger now writes into a folder, new fitness model module, various fixes.
Release 2.6.1 allow possibly much larger condition numbers, fix corner case with growing more-to-write list.
Release 2.6.0 allows initial solution x0 to be a callable.
Version 2.4.2 added the function cma.fmin2 which, similar to cma.purecma.fmin, returns (x_best:numpy.ndarray, es:cma.CMAEvolutionStrategy) instead of a 10-tuple like cma.fmin. The result 10-tuple is accessible in es.result:namedtuple.
Version 2.4.1 included bbob testbed.
Version 2.2.0 added VkD CMA-ES to the master branch.
Version 2.* is a multi-file split-up of the original module.
Version 1.x.* is a one file implementation and not available in the history of this repository. The latest 1.* version 1.1.7 can be found here.