pycma        

[BibTeX] cite as:

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

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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:

• A quick start guide with a few usage examples

• The above notebooks folder has some example code in Jupyter notebooks

• The API Documentation

• Hints for how to use this (kind of) optimization module in practice

• FAQs and HowTos (under development).

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.