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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# rmoo - R Multi-Objective Optimization <img src="man/figures/logo.png" align="right" width="150px" />
<!-- badges: start -->
[](https://github.com/Evolutionary-Optimization-Laboratory/rmoo/actions/)
[](https://app.codecov.io/gh/Evolutionary-Optimization-Laboratory/rmoo?branch=master)
[](https://CRAN.R-project.org/package=rmoo/)
[](https://lifecycle.r-lib.org/articles/stages.html#stable/)
<!-- badges: end -->
## Overview
`rmoo` is a non-dominated sorting based multi-objective optimization package
built upon the ['GA' package](https://CRAN.R-project.org/package=GA). It provides a comprehensive, flexible, and modular
framework for multi/many-objective optimization. Users have a wide range of
configuration options at their disposal, including the representation of real-values,
permutations, and binaries.
The algorithms available in `rmoo` include GA, NSGA-I, NSGA-II, R-NSGA-II, and NSGA-III.
## Installation
You can install the **stable** version on [R CRAN](https://cran.r-project.org/package=rmoo):
``` r
install.packages("rmoo")
```
Or you can install the **development** version from [GitHub](https://github.com/Evolutionary-Optimization-Laboratory/rmoo):
``` r
# install.packages("devtools")
devtools::install_github("Evolutionary-Optimization-Laboratory/rmoo")
```
## Usage
All the algorithms implemented in `rmoo` are called throught rmoo function.
It will receive all the necessary parameters for execution.
In this simple example, we solve the DTLZ1 problem, using the NSGA-III:
``` r
library(rmoo)
DTLZ1 <- function (x, nobj = 3, ...)
{
if (is.null(dim(x))) {
x <- matrix(x, 1)
}
n <- ncol(x)
y <- matrix(x[, 1:(nobj - 1)], nrow(x))
z <- matrix(x[, nobj:n], nrow(x))
g <- 100 * (n - nobj + 1 + rowSums((z - 0.5)^2 - cos(20 *
pi * (z - 0.5))))
tmp <- t(apply(y, 1, cumprod))
tmp <- cbind(t(apply(tmp, 1, rev)), 1)
tmp2 <- cbind(1, t(apply(1 - y, 1, rev)))
f <- tmp * tmp2 * 0.5 * (1 + g)
return(f)
}
ref_points <- generate_reference_points(3,12)
result <- rmoo(fitness = DTLZ1,
type = "real-valued",
algorithm = "NSGA-III",
lower = c(0,0,0),
upper = c(1,1,1),
monitor = FALSE,
summary = FALSE,
parallel = FALSE,
nObj = 3,
reference_dirs = ref_points,
popSize = 92,
maxiter = 300)
```
The rmoo package has a set of S4 method that can help the user to visualize,
analysis, and interpret the results.
We are going to show how use the `plot` method, since depending on the parameter
it will display difference plots, e.g. passing "pcp" as parameter for type, the
method displays the Parallel Coordinate Plot:
``` r
plot(result, type="pcp")
```
<!-- -->
Another example of plot method is when no parameters are passed, the method by
default display the scatter plot. It evaluates the result dimensionality and
depending on this, it will display 2-D, 3-D or Pairwise Scatter Plot.
``` r
#Scatter without optimal points
plot(result)
```
<!-- -->
When displaying the scatter plot, reference points can be passed as parameters
of the optimal argument, allowing the results to be compared to them:
``` r
#Scatter with optimal points (Using reference points as optimal points)
plot(result, optimal = result@reference_points)
```
<!-- -->
Other plots available in `rmoo` are heatmap and polar coordinate, both allow the user
to view specific solutions.
``` r
#Polar Coordinates
plot(result, type="polar", individual = c(1:3))
```
<!-- -->
``` r
#Heatmap Plot
plot(result, type="heatmap", individual = c(1:3))
```
<!-- -->
Other methods available in `rmoo` that may be of interest are `getFitness()`,
`getPopulation()`, `getMetrics()`, `print()`, `summary()` and others.
We do not show the functionality of all these functions since they will be
detailed in depth in the future article and vignettes.
## Citation
Until the submission of the formal article that introduces **rmoo**, please cite using:
> Benitez F., Pinto-Roa Diego P. (2023). _rmoo: Multi-Objective Optimization in R_.
> R package version 0.2.3 <https://CRAN.R-project.org/package=rmoo/>.
BibTeX entries for LaTeX users:
```
@Manual{,
title = {{rmoo}: Multi-Objective Optimization in R},
author = {Francisco Benitez and Diego P. {Pinto-Roa}},
year = {2023},
note = {R package version 0.2.3},
url = {https://CRAN.R-project.org/package=rmoo/},
}
```
or
```
@INPROCEEDINGS{10418638,
author={Francisco, J. Benítez and Diego, P. Pinto-Roa and Miguel, García-Torres and Parameshachari, B. D.},
booktitle={2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)},
title={A Hybrid Approach for Many-Objective Feature Selection in Intrusion Detection on Windows Operating Systems},
year={2023},
volume={},
number={},
pages={1-6},
doi={10.1109/CHILECON60335.2023.10418638/}}
```