Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
R
data
docs
images
inst/flaccogui
man-roxygen
man
tests
vignettes
.Rbuildignore
.travis.yml
DESCRIPTION
LICENSE
NAMESPACE
NEWS
README.md
appveyor.yml

README.md

flacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems

CRAN Status Badge GitHub Status Badge CRAN Downloads Research software impact Tutorial Web-GUI

Build Status Build status Coverage Status codecov.io

  • If you like our package, please star it on Github.

Introduction

flacco is a collection of features for Explorative Landscape Analysis (ELA) of single-objective, continuous (Black-Box-)Optimization Problems. It allows the user to quantify characteristics of an (unknown) optimization problem's landscape.

Features, which used to be spread over different packages and platforms (R, Matlab, python, etc.), are now combined within this single package. Amongst others, this package contains feature sets, such as ELA, Information Content, Dispersion, (General) Cell Mapping or Barrier Trees.

Furthermore, the package provides a unified interface for all features -- using a so-called feature object and (if required) control arguments. In total, the current release (1.7) consists of 17 different feature sets, which sum up to approximately 300 features.

In addition to the features themselves, this package also provides visualizations, e.g. of the cell mappings, barrier trees or information content:

  • 3D-Barrier Tree:

Examplary Barrier Tree

  • Cell Mapping:

Examplary Cell Mapping

  • Information Content Plot:

Examplary Info Content

Quickstart

If you want to get started quickly, have a look at the flacco tutorial.

library(flacco)

## (1) Create some example-data
X = createInitialSample(n.obs = 500, dim = 2)
f = function(x) sum(sin(x) * x^2 + (x - 0.5)^3)
y = apply(X, 1, f)

## (2) Compute the feature object
feat.object = createFeatureObject(X = X, y = y)

## (3) Have a look at feat.object
print(feat.object)

## (4) Check, which feature sets are available
listAvailableFeatureSets()

## (5) Calculate a specific feature set, e.g. the ELA meta model
featureSet = calculateFeatureSet(feat.object, set = "ela_meta")

Installation Instructions

  • If you want to use the version from CRAN, you should download it from here and ideally install it along with all its dependencies:

    install.packages("flacco", dependencies = TRUE)
  • However, if you want to use the development version, you can download it with the following command:

    devtools::install_github("kerschke/flacco")

flacco-GUI

We have started to implement a graphical user interface (GUI) for our package. Its online-version is hosted here.

Alternatively, you can start it from within R:

library(flacco)
runFlaccoGUI()

News

Contact

If you have any suggestions or ideas (e.g. for new features), or if you encounter any problems while running the code, please use the issue tracker or send me an e-mail (kerschke@uni-muenster.de).