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### Highlights from SpeedyGA: A Fast Simple Genetic Algorithm

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# SpeedyGA: A Fast Simple Genetic Algorithm

31 May 2007 (Updated 21 Nov 2010)

A vectorized implementation of a simple genetic algorithm in Matlab

File Information
Description

SpeedyGA is a vectorized implementation of a genetic algorithm in the Matlab programming language. Without bells and whistles, it faithfully implements the specification for a Simple GA given on pgs 10, 11 of M. Mitchell's GA book. See comments in code for details.

This script has played a crucial part in the development of a new, unified explanation for the adaptive capacity of genetic algorithms (including ones with uniform crossover) called the Generative Fixation Hypothesis. See http://cs.brandeis.edu/~kekib/dissertation.html

Matlab is optimized for performing operations on arrays. Loops, especially nested loops, tend to run slowly in Matlab. It is possible to significantly improve the performance of Matlab programs by converting loops into array operations. This process is called vectorization. Matlab provides a rich set of functions and many expressive indexing schemes that make it possible to vectorize code. Such code not only runs faster, it is also shorter, and simpler to understand and change (provided that you know a little about Matlab of course).

Genetic Algorithms that are implemented in C/C++ or Java typically have multiple nested loops. Therefore direct ports of such implementations to Matlab will run very slowly. Many of the nested loops found in a typical GA implementation have been eliminated from SpeedyGA. The resulting code is short, fast and simple. It is indeed a delightful coincidence when the constructs of a programming language match a programming task so well that a program can be written this succinctly.

SpeedyGA is proof that Matlab is a useful language for the rapid prototyping of Genetic Algorithms. This, in addition to Matlab's extensive data visualization capabilities, make Matlab an extremely useful platform for the experimental analysis of GAs.

SpeedyGA has been created and tested under Matlab 7 (R14). Out of the box it evolves a population against the one-max fitness function. The royal-roads fitness function has also been included but is not currently being called. If you find SpeedyGA useful or find any bugs please let me know.

Enjoy!

p.s. For an experimental genetic algorithm which might significantly improve the quality of the solutions returned check out TurboGA (http://www.mathworks.com/matlabcentral/fileexchange/24053 )

Acknowledgements
MATLAB release MATLAB 7 (R14)
17 Jul 2012

Hey, nice code.
I got an error for small populations and small genomes, and solved it by changing line 132 from "fitnessVals=ones(popSize,1);" to "fitnessVals=ones(1,popSize);". Please let me know if that change is valid or Im braking something in the code!

26 Apr 2012

27 Sep 2011

super....

23 Jun 2011

Very nice work!!

13 Jan 2011
13 Jan 2011
09 Aug 2010
06 Aug 2010
08 Jul 2010

good thanks

19 Dec 2008

Good work, but can be improved more,

21 Oct 2008

awesome!!!!!!!!!

11 Jun 2007

09 Jul 2008

different titme

15 Dec 2008

pre-generated crossover and mutation masks, which significantly improves performance

16 Dec 2008

Added the option of visualizing bit frequencies (handy for studying GA dynamics)

19 Dec 2008

corrected a typo in the description field

31 Dec 2008

1) The best individual of each generation is no longer displayed
2) Plots the maximum and average fitness of each generation at the end of a run
3) Upon completion, returns the best individual of a run, and its fitness

04 Jan 2009

.

04 Feb 2009

SpeedyGA now runs as a script (easier to work with for research purposes). Without bells and whistles, it now faithfully implements the specification for a Simple GA given on pgs 10, 11 of M. Mitchell's GA book. See comments in code for details.

11 May 2009

Updated description

15 May 2009

updated description

21 Nov 2010

No updates to the code. Changed the description to mention the Generative Fixation Hypothesis