SpeedyGA: A Fast Simple Genetic Algorithm

A vectorized implementation of a simple genetic algorithm in Matlab
Updated 21 Nov 2010

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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.


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 )

Cite As

Keki Burjorjee (2024). SpeedyGA: A Fast Simple Genetic Algorithm (https://www.mathworks.com/matlabcentral/fileexchange/15164-speedyga-a-fast-simple-genetic-algorithm), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R14
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

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

updated description

Updated description

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.


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

corrected a typo in the description field

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

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

different titme