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GEATbx: Tutorial 1 Introduction

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1 Introduction

The GEATbx (Genetic and Evolutionary Algorithm Toolbox for use with Matlab) contains a broad range of tools for solving real-world optimization problems. They not only cover pure optimization, but also the preparation of the problem to be solved, the visualization of the optimization process, the reporting and saving of results, and as well as some other special tools.

This Tutorial provides an introduction to the main GEATbx functions. The steps necessary for the efficient application of the GEATbx are explained. Nevertheless, this tutorial does not and cannot cover all the functions and aspects of the GEATbx. (Remember, you can always refer to the implemented code.)

The first steps for a 'Quick Start' are described in Chapter 2 and some examples are given. You can try these examples immediately and see the results seconds later. The examples use some of the GEATbx demos, giving a head start to those eager to try out the GEATbx.

From there on you have at least two ways of proceeding with this tutorial.

When using the GEATbx you need to know how to implement your problem ('Writing Objective Functions'). The procedure for doing so is described in Chapter 3.

Another important aspect which must be considered is the format of the 'Variable Representation', see Chapter 4.

The structure of the GEATbx is described in 'Overview of GEA Toolbox Structure' in an explanation of the 'Calling Tree' of the functions, see Chapter 5. A brief overview of the interconnection between the GEATbx functions is also provided in this chapter. The GEATbx functions follow a 'Naming Convention', see Section 5.1.

Multi-objective optimization is fully integrated into the standard behavior of the GEATbx. The additional aspects to switch it on and control it are explained in Chapter 6.

The 'Data Structures of the GEATbx' are documented in Chapter 8.

All the direct algorithm documentation is done inside the Matlab m-files (help name_of_m_file). An extensive help text is provided for each function explaining the purpose and syntax and including illustrative examples. The M-function index (only in the on-line documentation) contains this information and additionally the dependencies between the functions (which function calls which other functions).

Years of work using the GEATbx to solve real-world problems has shown us, amongst other things, that the approach to new optimization problems is always one of the most important aspects. Chapter 9, 'How to Approach new Optimization Problems', explains a number of tips and steps.

If any part of the documentation does not address your problem, you can always contact the technical support for the GEATbx by email (

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GEATbx: Main page  Tutorial  Algorithms  M-functions  Parameter/Options  Example functions 

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