code release

SpeedyGA v1.3

Keki1 Comment

Download the latest version of SpeedyGA (version 1.3) from Matlab Central’s File Exchange or from Google Code.

Version 1.3 of speedyGA is faithful to the specification for a simple GA given on page 10 of Melanie Mitchell’s book “Introduction to Genetic Algorithms”; that is if none of the bells and whistles (mask repositories, stochastic universal sampling, and sigma scaling) are used. SpeedyGA has also been changed from a function to a script. This makes it easier to inspect variables and plot data after a run has completed.

[Update Jan 27, 2013: SpeedyGA.py is a port of SpeedyGA to Python]

SpeedyGA v1.3

VectorGA is now SpeedyGA

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With VectorGA being downloaded from Matlab Central over 611 times in the past 30 days, I thought I’d release some of the tweaks I’ve been using to speed it up. I’m calling the result SpeedyGA .

Download SpeedyGA from Matlab Central’s File Exchange or from Google Code.

Release notes:

SpeedyGA is a vectorized implementation of the Simple Genetic Algorithm in the Matlab programming language.

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 vectorGA useful or find any bugs please let me know.

Enjoy!

SpeedyGA is a revision of the old VectorGA. SpeedyGA has (not surprisingly) been further optimized for speed.

Changes since VectorGA

  1. Added mutation  and crossover mask pregeneration
  2. Added the option to visualize the changing bit frequencies of a population (very handy for understanding GA dynamics)
VectorGA is now SpeedyGA

VectorGA: A Vectorized Implementation of a Genetic Algorithm in Matlab

Keki6 Comments

VectorGA is a vectorized implementation of a genetic algorithm in the Matlab programming language.

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 VectorGA. The resulting code is short, fast and simple. It is a neat coincidence when the constructs of a programming language match a programming task so well that a program can be written this succintly.

VectorGA 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.

VectorGA 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 vectorGA useful or find any bugs please let me know.

Enjoy!

Original vectorGA release site

The latst version of VectorGA is SpeedyGA

VectorGA: A Vectorized Implementation of a Genetic Algorithm in Matlab