https://github.com/jakobbossek/ecr3vis
An evolutionary algorithm framework for R (currently limited to performance assessment of multi-objective randomized search heuristics).
https://github.com/jakobbossek/ecr3vis
evolutionary-algorithms evolutionary-algorithms-framework genetic-algorithm multi-objective-optimization performance-assessment visualization
Last synced: 3 months ago
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An evolutionary algorithm framework for R (currently limited to performance assessment of multi-objective randomized search heuristics).
- Host: GitHub
- URL: https://github.com/jakobbossek/ecr3vis
- Owner: jakobbossek
- License: gpl-3.0
- Created: 2021-05-16T10:44:54.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-07-12T14:43:30.000Z (almost 4 years ago)
- Last Synced: 2025-02-13T22:42:28.677Z (4 months ago)
- Topics: evolutionary-algorithms, evolutionary-algorithms-framework, genetic-algorithm, multi-objective-optimization, performance-assessment, visualization
- Language: C
- Homepage: https://jakobbossek.github.io/ecr3vis/
- Size: 1.12 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 23
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS
- License: LICENSE
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README
---
output: github_document
---
# ecr3vis: ecr3 visualization
**Visit the [package website](https://jakobbossek.github.io/ecr3vis/).**
**NOTE:** __Under heavy development!!! :construction:__\
It is likely that **ecr3vis** will be merged/renamed into **ecr3** in the course of development.
[](https://www.repostatus.org/#active)
[](https://GitHub.com/jakobbossek/ecr3vis)
[](https://github.com/jakobbossek/ecr3vis/actions)
[](https://codecov.io/gh/jakobbossek/ecr3vis)
[](https://cran.r-project.org/package=ecr3vis)
## Introduction
**ecr3vis** is the visualization module of **ecr3** (under development). It offers a collection of functions for the visualization of results of randomized search heuristics. The focus is on multi-objective problems. The package includes 2d- and 3d-scatter-plots, parallel coordinate plots (PCP), heatmaps etc.
## Example
In the following we demonstrate how to build a simple mutation-based EA to optimize the Pseudo-boolean function ONEMAX which counts the number of ones in a bistring of length n, i.e., the optimum is obviously the all-ones bit-string.
We first define the fitness function that guides the evolutionary search.
```r
library(ecr3vis)
library(tidyverse)
# import sample data-set
data(emoas_on_zdt)
tbl = filter(emoas_on_zdt, repl == 1L)
plot_scatter2d(tbl, colour = "algorithm")
# Add third objective
tbl$y3 = tbl$y2 + 1
plot_heatmap(tbl)
plot_radar(tbl[1:3, ])
plot_radar(tbl[1:3, ]) + facet_grid(. ~ nr)
```
## Development Team
The package is a one-man project by [Jakob Bossek](https://researchers.adelaide.edu.au/profile/jakob.bossek) at the moment of writing. However, the package interfaces some neat implementations of various other people (see DESCRIPTION file for details).
## How to contribute?
You can contribute by identifing annoying bugs in the [issue tracker](http://github.com/jakobbossek/ecr3vis). This is also the preferred place to ask questions and raise feature requests. Moreover, users can contribute even more by [forking](https://help.github.com/en/github/getting-started-with-github/fork-a-repo) the ecr3vis repository, implementing feautures or bugfixes and raising a [pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests).
## Installation Instructions
The package will be available at [CRAN](http://cran.r-project.org) *when it is done*. 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:
```{r, eval = FALSE}
remotes::install_github("jakobbossek/ecr3vis")
```
## Getting help
Please address questions and missing features about the *ecr3vis* as weell as annoying bug reports in the [issue tracker](https://github.com/jakobbossek/ecr3vis/issues). Pay attention to explain your problem as good as possible. At its best you provide an example, so I can reproduce your problem quickly. Please avoid sending e-mails.
## Related Software
There are many alternatives in both R and other languages. We list some examples in the following:
* [**ecr**](https://cran.r-project.org/package=ecr) *Evolutionary Computation in R*, short **ecr**, is the predecessor of **ecrvis**.
* [**eaf**](https://cran.r-project.org/package=eaf) R package with functions to plot *Empirical Attainment Functions* (EAF) and EAF-differences. In addition, the package offers various multi-objective performance indicators.
* [**mco**](https://cran.r-project.org/package=mco) A R package with focus on the rapid implementation of multi-objective evolutionary algorithms only.
* [**emoa**](https://cran.r-project.org/package=emoa) Another R package with focus on the development of multi-objective evolutionary algorithms (same author as in **mco**).
* [**GA**](https://cran.r-project.org/package=GA) R package with some building blocks for evoltionary algorithms. Also offers the possibility for island model EAs. However, restricted to standard representations and single-objective problems.
* [**MaOEA**](https://cran.r-project.org/package=MaOEA): A set of evolutionary algorithms to solve many-objective optimization (i.e. problems with more than three objectives).
* [**pymoo**](https://pymoo.org) A quite new, yet comprehensive, Python library for evolutionary multi-objective optimization.
* [**DEAP**](https://github.com/deap/deap) Another prominent and established Python framework on evolutionary computation with a wide range of functionality.