Evolutionary Algorithms for MATLAB (incl. Genetic Algorithms and Genetic Programming)
Evolutionary Algorithms are the common term used for algorithms based on principles of nature (evolution, genetic).
Evolutionary Algorithms contain genetic algorithms, evolution strategies, evolutionary programming and genetic programming.
Other Implementations of Genetic Algorithms and Genetic Programming in Matlab
Genetic Algorithm Toolbox for use with MATLAB, version 1.2
- Andrew Chipperfield,
Hartmut Pohlheim and
University of Sheffield, UK
- April 1994
- A. J. Chipperfield, P. J. Fleming, H. Pohlheim
and C. M. Fonseca, "Genetic Algorithm
Toolbox User's Guide", ACSE Research Report
No. 512, University of Sheffield, 1994.
- Key Features:
- Support for binary, integer and real-valued representations.
- A wide range of genetic operators.
- High-level entry points to most low-level functions.
- Many variations on the standard GA.
- Support for virtual multiple subpopulations.
Evolutionary Computation in Control Systems Engineering
(University of Sheffield, UK)
- Genetic Algorithm Optimization Toolbox (GAOT)
- Chris Houck, Jeff Joines and Mike Kay;
North Carolina State University, USA
- April 1996
- GAOT implements simulated evolution in the Matlab environment using both binary and real representations.
This implemenation is very flexible in the genetic operators, selection functions, termination functions
as well as the evaluation functions that can be used.
- Houck, C., Joines, J., and Kay, M., "
A Genetic Algorithm for Function Optimization: A Matlab Implementation",
NCSU-IE TR 95-09, 1995. (alternate ftp access)
- FlexTool (GA): Genetic Algorithm Toolbox for Matlab Users
- Flexible Intelligence Group, LLC, USA
- 1996 (newer version ???)
- Unique Features
- Niching module : to identify multiple solutions
- Clustering module : Use separately or with Niching module
- Optimization : Single and Multiple Objectives
- Flex-GA : Very fast proprietary learning algorithm
- GA : Modular, User Friendly, and System Transparent
- GUI : Easy to use, user friendly
- Help : Online
- Tutorial : Hands-on tutorial, application guidelines
- Parameter Settings : Default parameter settings for the novice
- General : Statistics, figures, and data collection
- GA options : generational, steady state, micro, Flex-GA
- Coding schemes : include binary, logarithmic, real
- Selection : tournament, roulette wheel, ranking
- Crossover : include 1, 2, multiple point crossover
- Genetic Algorithm M-files
- Genetic Algorithm Toolbox for Matlab
- Michael B. Gordy; Federal Reserve Board
- February 1996
- simple genetic algorithm implementation, one m-file
- Genetic Programming with Matlab
- What used to be the Symbolic Optimisation Research Group (SORG)
at the University of Newcastle upon Tyne no longer exists.
The code is not available at the moment but there will be a new version
available in the near future. (2001/06, Dominic Searson, Advanced Process Control Group)
Other pages providing an overview of Evolutionary / Genetic Algorithms (EA) tools in Matlab
Go to: Main page of GEATbx
last update: 2001/07