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Table of Contents
1 Tutorial.....................................................................................................1-1
Installation ..................................................................................................1-2
An Overview of Genetic Algorithms .........................................................1-3
What are Genetic Algorithms .........................................................1-3
GAs versus Traditional Methods ....................................................1-5
Major Elements of the Genetic Algorithm ................................................1-6
Population Representation and Initialisation ..................................1-6
The Objective and Fitness Functions..............................................1-8
Selection .........................................................................................1-9
Roulette Wheel Selection Methods ....................................1-10
Stochastic Universal Sampling ..........................................1-12
Crossover ........................................................................................1-12
Multi-point Crossover.........................................................1-12
Uniform Crossover ............................................................1-13
Other Crossover Operators .................................................1-14
Intermediate Recombination...............................................1-14
Line Recombination ...........................................................1-15
Discussion ..........................................................................1-15
Mutation .........................................................................................1-16
Reinsertion ......................................................................................1-18
Termination of the GA ...................................................................1-18
Data Structures ...........................................................................................1-20
Chromosomes .................................................................................1-20
Phenotypes .....................................................................................1-20
Objective Function Values .............................................................1-21
Fitness Values .................................................................................1-22
Support for Multiple Populations ..............................................................1-23
Examples ....................................................................................................1-26
The Simple GA ..............................................................................1-26
A Multi-population GA ..................................................................1-30
Demonstration Scripts.....................................................................1-36
References...................................................................................................1-37
2 Reference..............................................................................................2-1
Genetic Algorithm Toolbox User’s Guide 1-1
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1 Tutorial
MATLAB has a wide variety of functions useful to the genetic algorithm
practitioner and those wishing to experiment with the genetic algorithm for the
first time. Given the versatility of MATLAB’s high-level language, problems can be
coded in m-files in a fraction of the time that it would take to create C or Fortran
programs for the same purpose. Couple this with MATLAB’s advanced data
analysis, visualisation tools and special purpose application domain toolboxes and
the user is presented with a uniform environment with which to explore the
potential of genetic algorithms.
The Genetic Algorithm Toolbox uses MATLAB matrix functions to build a set of
versatile tools for implementing a wide range of genetic algorithm methods. The
Genetic Algorithm Toolbox is a collection of routines, written mostly in m-files,
which implement the most important functions in genetic algorithms.

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