GEATbx: Main page  Tutorial  Algorithms  M-functions  Parameter/Options  Example functions  www.geatbx.com 

GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with MATLAB
Documentation

Version 3.80 (released December 2006)
Author: Hartmut Pohlheim

The Genetic and Evolutionary Algorithm Toolbox (GEATbx) implements a wide range of genetic and evolutionary algorithms to solve large and complex real-world problems. Many ready-to-run demos and examples are included.

The documentation of the GEA Toolbox contains a 1 Tutorial, an 2 Introduction to Evolutionary Algorithms and a large 3 Reference section.

A first overview can be found in 4 Features of the GEATbx and 5 Implementation of the GEATbx.

The 6 Installation of the GEATbx is simple and can be done in a minute.

The Release Notes of the GEATbx give a compact overview of the new features and changes between versions.

1 Tutorial

2 Introduction to Evolutionary Algorithms

Explains the structure of Evolutionary Algorithms and their operators as implemented in the GEATbx. Can also be used to understand the working of Evolutionary Algorithms.

3 Reference

Contains a in-deep explanation of all the available parameter setting and options of the GEATbx and the documentation of the implemented functions directly created from the source of the m-files.

4 Features of the GEATbx

5 Implementation of the GEATbx

6 Installation

Extract the compressed files into the desired directories. The directory structure should be kept intact. It is recommended that the files for the Genetic and Evolutionary Algorithm Toolbox are stored in a directory named geatbx off the main matlab/toolbox directory.

All the paths of the GEA toolbox must be included in the Matlab search path. Really, include all paths.

Restart Matlab and the functionality of the GEATbx should be available. Test it by running one of the demo/example scripts (for instance demofun1.m, demo*.m in subdirectory scripts).

7 Use of documentation

Please consider the following: The use of this documentation is allowed only for personal information. Additionally, you can copy this documentation in unchanged form on your internal net for internal use as documentation of the Genetic and Evolutionary Algorithm Toolbox. It is prohibited to use the documentation or part of it in changed form. (However, if you want to use parts of it, text or graphics for lectures, another documentation or anything else, please contact the author.)

8 Why a toolbox for Evolutionary Algorithms for Matlab?

During the last years the interest in Genetic and Evolutionary Algorithms (EA: Evolutionary Algorithms) raised steadily. Compared to traditional search and optimization procedures, such as calculus-based and enumerative strategies, Evolutionary Algorithms are robust, globally oriented and generally more straightforward to apply in situations where there is little or no a priori knowledge about the problem to solve. As Evolutionary Algorithms require no derivative information or formal initial estimates of the solution, and because they are stochastic in nature, Evolutionary Algorithms are capable of searching the solution space with more likelihood of finding the global optimum.

Matlab has become a de-facto standard in a wide range of technical applications. Many areas are catered for by a wide range of toolboxes along with extensive visualization and analysis tools. In addition, Matlab has an open and extensible architecture allowing individual users to develop further routines for their own applications. These qualities provide a uniform and familiar environment on which to build genetic and evolutionary algorithm tools.

GEATbx: Main page  Tutorial  Algorithms  M-functions  Parameter/Options  Example functions  www.geatbx.com 

This document is part of version 3.8 of the GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with Matlab - www.geatbx.com.
The Genetic and Evolutionary Algorithm Toolbox is not public domain.
© 1994-2006 Hartmut Pohlheim, All Rights Reserved, (support@geatbx.com).