Hartmut Pohlheim, Version 3.30 (November 2000)
The Genetic and Evolutionary Algorithm Toolbox (GEATbx) provides a set of versatile tools for implementing a wide range of genetic and evolutionary algorithm methods. A first overview can be found in Features and Implementation of the GEA Toolbox.
The documentation of the GEA Toolbox contains a Tutorial, an Introduction to Evolutionary Algorithms and a large Reference section. The Tutorial explains the usage of the GEATbx, including quick start, how to write own objective functions and many examples of objective functions. The Introduction to Evolutionary Algorithms explains the structure of Evolutionary Algorithms and their operators. The Reference section contains three parts: in deep explanation of all the available parameter setting and options of the GEATbx and the documentation directly created from the source of the m-files.
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 (demo*.m/scr2* in subdirectory scripts).
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.)
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 Computer Aided Control System Design (CACSD). Many areas are catered for by a wide range of toolboxes, notably the Control System, Neural Network and Optimization Toolboxes, and the Simulink simulation package 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.