Food Web Genetic Algorithms
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README.md

Genetic algorithms for simulating optimal food web structures (fwga)

This README is for the R package fwga (food web genetic algorithms) (see also Yen JDL, et al., Highly connected food webs maximize robustness but not ecosystem throughput, in review).

Copyright © 2015, Jian Yen


Licence details

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.


Overview

fwga is a collection of R functions for simulating optimal food web (interaction network) structures according to one or more fitness functions. All functions in fwga are written and tested in R 3.1.2.

Several additional packages are required (see Installation, below) but all are available through CRAN.

Created 21 May 2015

Last updated 21 May 2015


Installation

fwga is distributed as an R package (in binary and source form) but is not currently available through the CRAN. The current version of fwga has been tested only on OSX 10.10.3 but should be suitable for Windows and other Unix systems. A binary has been created for OSX and Windows.

fwga imports functions from several packages (see Depends and Imports in the DESCRIPTION file) and these packages must be installed for fwga to install and load correctly. All packages are available through the CRAN and should be easy to install.

Install from binary

OSX users

Download the file fwga_x.x.tgz into a local directory on your computer (replace x.x with the current version). Place the file fwga_x.x.tgz in the current working directory (where x.x is replaced with the current version number) and type

install.packages("fwga_x.x.tgz", repos=NULL)

into the R console to install the fwga R package. This package then can be loaded in R using library(fwga).

Windows users

Download the file fwga_x.x.zip into a local directory on your computer (replace x.x with the current version). Place the file fwga_x.x.zip in the current working directory (where x.x is replaced with the current version number) and type

install.packages("fwga_x.x.zip", repos=NULL)

into the R console to install the fwga R package. This package then can be loaded in R using library(fwga).

Install from source

Download the file fwga_x.x.tar.gz into a local directory on your computer (replace x.x with the current version).

Because fwga is installed from source you will also need an appropriate C and C++ compiler installed. Easily installed options are gcc (OSX users) and Rtools (Windows users). If you're unsure of whether you need to install a C/C++ compiler, you can try installing the fwga package anyway; if you do not get any errors then no compiler is needed.

Once the appropriate packages have been installed, you simply need to place the file fwga_x.x.tar.gz in the current working directory (where x.x is replaced with the current version number) and use

install.packages("fwga_x.x.tar.gz", repos=NULL, type="source")

to install the fwga R package. This package then can be loaded in R using library(fwga).

Errors during installation often are related to the installation of required packages. Restarting R and making sure all required packages can be loaded using library(pkgName) will identify missing or incorrectly installed packages.

Usage

Once fwga has been installed there is one main function to use: fwga. This function has been set up with reliable default settings and information about its use can be found by typing ?fwga in the R console.

The main settings to change are the number of iterations n.iter, the number of populations n.pop, the starting population suggest, and the desired fitness function/s fits. With default settings and enough iterations, the genetic algorithm should converge to optimal food-web structures. Convergence can be assessed by plotting the calculated fitness against iterations; type ?fwga into the R console for more information. Note that the fwga function minimizes the chosen fitness functions, so, if maximization is required, the fitness function must be multiplied by minus one.

Mathematical details of the genetic algorithm, along with an example of its application, are included in: Yen JDL, et al. (in review) Highly connected food webs maximize robustness but not ecosystem throughput.


Feedback

Please send comments and bug reports to jdl.yen@gmail.com