Portions of a generic genetic-algorithms library
Contents |
Introduction
Over the last few months I have become interested in problems associated with the modeling of non-linear systems. This interest has lead me to look into artificial neural networks, and how they can be trained using genetic algorithms. For those with an interest in this area, development environments like Matlab provide an ideal platform for quick implementation and testing. However, at deployment time, a Matlab script is of little use to clients with a limited budget, or to developers targeting some embedded platforms.
... to be continued ...
What is the motivation here? Off-line training of neural networks via genetic algorithms to model non-linear systems in a low cost environment.
Theoretical Background
Some Basic theory on genetic algorithms (perhaps some background on artificial neural networks, and artificial neural network training?)
Requirements
What should a generic genetic-algorithms library do, and why? How might this change?
Examples of Genetic-Algorithms Libraries
Describe the Matlab GA libraries, and how they are used. What other examples are there?
A First Attempt
This is the starting point - something I put together around 6 months ago.
My first working attempt at a generic genetic-algorithms library is shown in Fig. 1. This section will dissect and critique portions of the initial library design.
Creating New Candidates
A modified Factory pattern is used here, this was taken from a Microsoft Patterns and Practices article by Doug Purdy.
Manipulating Chromosomes
A chromosome is stored in a Candidate object as a List of bytes. The responsibility of manipulating chromosomes is shared by IMutator implementers.
Calculating Fitness
Fitness calculations are known about by
- the GeneticAlgorithms class
- the Generation class
- the Candidate class
Reporting Statistics
Generational statistical information is known about by
- the GeneticAlgorithms class
- the Generation class
