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https://github.com/jakobbossek/cmaesr

Covariance Matrix Adaption - Evolutionary Strategy in R.
https://github.com/jakobbossek/cmaesr

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Covariance Matrix Adaption - Evolutionary Strategy in R.

Host: GitHub
URL: https://github.com/jakobbossek/cmaesr
Owner: jakobbossek
License: other
Created: 2014-08-15T12:42:05.000Z (almost 10 years ago)
Default Branch: master
Last Pushed: 2016-12-04T10:08:09.000Z (over 7 years ago)
Last Synced: 2023-11-20T15:22:40.154Z (7 months ago)
Language: R
Size: 369 KB
Stars: 8
Watchers: 4
Forks: 2
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE

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alex-mikhalev-awesome-stars - cmaesr - Covariance Matrix Adaption - Evolutionary Strategy in R. (R)

README

        # cmaesr: Covariance Matrix Adaptation - Evolution Strategy in R

[![CRAN Status Badge](http://www.r-pkg.org/badges/version/cmaesr)](http://cran.r-project.org/web/packages/cmaesr)

[![CRAN Downloads](http://cranlogs.r-pkg.org/badges/cmaesr)](http://cran.rstudio.com/web/packages/cmaesr/index.html)

[![Build Status](https://travis-ci.org/jakobbossek/cmaesr.svg?branch=master)](https://travis-ci.org/jakobbossek/cmaesr)

[![Build status](https://ci.appveyor.com/api/projects/status/eu0nns2dsgocwntw/branch/master?svg=true)](https://ci.appveyor.com/project/jakobbossek/cmaesr/branch/master)

[![Coverage Status](https://coveralls.io/repos/jakobbossek/cmaesr/badge.svg)](https://coveralls.io/r/jakobbossek/cmaesr)

## Description

The *cmaesr* package implements the popular [Covariance Matrix Adaptation - Evolution Strategy](https://www.lri.fr/~hansen/cmatutorial.pdf) [2, 3] optimizer for numerical optimization problems in pure R. The main features of the package are:

* Extensible S3 based system for stopping conditions.

* Possibility to enable [restarting](https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0CDgQFjADahUKEwiHyr2B3-fIAhVEOBoKHZFPBgs&url=https%3A%2F%2Fwww.lri.fr%2F~hansen%2Fcec2005ipopcmaes.pdf&usg=AFQjCNGwtYnwiRizaVZzbrfeXZjj-DYLtg&sig2=kMpEze_3Qe965UZ08wl-sw&bvm=bv.106130839,d.bGg) [1] with flexible declaration of restart-triggering stopping conditions.

* Nice visualization of 2D optimization runs for, e.g., teaching purposes.

![Visualization of the distribution in an examplary optimization run on the sphere function](https://github.com/jakobbossek/cmaesr/blob/master/images/sphere_cma_es_visualization.png)

[1] Auger and Hansen (2005). *A Restart CMA Evolution Strategy With Increasing

Population Size.* In IEEE Congress on Evolutionary Computation, CEC 2005, Proceedings, pp. 1769-1776.

[2] N. Hansen (2006). *The CMA Evolution Strategy: A Comparing Review.* In J.A. Lozano, P. Larranaga, I. Inza and E. Bengoetxea (Eds.). Towards a new evolutionary computation. Advances in estimation of distribution algorithms. Springer, pp. 75-102.

[3] Hansen and Ostermeier (1996). *Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation.* In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pp. 312-317.

## Installation Instructions

The package will be available in a first version at [CRAN](http://cran.r-project.org) soon. If you are interested in trying out and playing around with the current github developer version use the [devtools](https://github.com/hadley/devtools) package and type the following command in R:

```splus

devtools::install_github("jakobbossek/cmaesr")

```

## Example

Assume we want to minimize the 2D [Ackeley Function](http://www.sfu.ca/~ssurjano/ackley.html). To accomplish this task with *cmaesr* we need to define the objective function as a [smoof](https://github.com/jakobbossek/smoof) function. This function is then passed with some control arguments to the main function of the packge.

```splus

library(cmaesr)

# first generate the objective smoof function

fn = makeAckleyFunction(dimensions = 2L)

res = cmaes(

    fn, 

    monitor = makeSimpleMonitor(),

    control = list(

        sigma = 1.5, # initial step size

        lambda = 50, # number of offspring

        stop.ons = c(

            list(stopOnMaxIters(100)), # stop after 100 iteration ...

            getDefaultStoppingConditions() # ... or after some default stopping conditions

        )

    )

)

print(res)

```

For 2D functions a monitor for visualization is included.

```splus

library(cmaesr)

# generate the objective function

fn = makeSphereFunction(dimensions = 2L)

res = cmaes(

    fn,

    monitor = makeVisualizingMonitor(

        show.distribution = TRUE, show.last = TRUE

    ),

    control = list(

        sigma = 1, lambda = 100,

        stop.ons = list(stopOnMaxIters(15L))

    )

)

```

## Contact

Please address questions and missing features about the **cmaesr package** to the author Jakob Bossek . Found some nasty bugs? Please use the [issue tracker](https://github.com/jakobbossek/cmaesr/issues) for this. Pay attention to explain the problem as good as possible. At its best you provide an example, so I can reproduce your problem.