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README.Rmd
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README.Rmd
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---
output: github_document
---
# smoof: Single- and Multi-Objective Optimization Test Functions
Visit the [package website](https://jakobbossek.github.io/smoof/)
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This package offers an interface for objective functions in the context of (multi-objective) global optimization. It conveniently builds up on the S3 objects, i. e., an objective function is a S3 object composed of a descriptive name, the function itself, a parameter set, box constraints or other constraints, number of objectives and so on. Moreover, the package contains generators for a load of both single- and multi-objective optimization test functions which are frequently being used in the literature of (benchmarking) optimization algorithms.
The bi-objective ZDT function family by Zitzler, Deb and Thiele is included as well as the popular single-objective test functions like De Jong's function, Himmelblau function and Schwefel function. Moreover, the package offers a R interface to the C implementation of the *Black-Box Optimization Benchmarking* (BBOB) [set of noiseless test functions](http://coco.gforge.inria.fr/doku.php?id=bbob-2009-downloads).
## Installation instructions
Visit the [package repository on CRAN](https://CRAN.R-project.org/package=smoof). If you want to take a glance at the developement version install the github developement version by executing the following command:
```r
devtools::install_github("jakobbossek/smoof")
```
## Example
### Use a build-in generator
Assume the simplifying case where we want to benchmark a set of optimization algorithms on a single objective instance. We decide ourselves for the popular 10-dimensional Rosenbrock banana function. Instead of looking up the function definition, the box constraints and where the global optimum is located, we simply generate the function with **smoof** and get all the stuff:
```r
library(ggplot2)
library(plot3D)
obj.fn = makeRosenbrockFunction(dimensions = 2L)
print(obj.fn)
print(autoplot(obj.fn))
plot3D(obj.fn, length.out = 50L, contour = TRUE)
```
### Set up an objective function by hand
Let us consider the problem of finding the (global) minimum of the multimodal target function f(x) = x sin(3x) on the closed intervall [0, 2PI]. We define our target function via the ```makeSingleObjectiveFunction()``` method providing a name, the function itself and a parameter set. We can display the function within the box constraints with ggplot.
```r
library(ggplot2)
obj.fn = makeSingleObjectiveFunction(
name = "My fancy function name",
fn = function(x) x * sin(3*x),
par.set = makeNumericParamSet("x", len = 1L, lower = 0, upper = 2 * pi)
)
print(obj.fn)
print(getParamSet(obj.fn))
print(autoplot(obj.fn))
```
The [ecr](https://github.com/jakobbossek/ecr2) package for evolutionary computing in R needs builds upon smoof functions.
## Citation
Please cite my [R Journal paper](https://journal.r-project.org/archive/2017/RJ-2017-004/index.html) in publications. Get the information via `citation("smoof")` or use the following BibTex entry:
```
@Article{,
author = {Jakob Bossek},
title = {smoof: Single- and Multi-Objective Optimization Test Functions},
year = {2017},
journal = {The R Journal},
url = {https://journal.r-project.org/archive/2017/RJ-2017-004/index.html},
}
```
## Contact
Please address questions and missing features about the **smoof package** to the author Jakob Bossek <[email protected]>. Found some nasty bugs? Please use the [issue tracker](https://github.com/jakobbossek/smoof/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.