A fast, extensible, vectorized genetic algorithm with uniform crossover, sigma scaling, and stochastic universal sampling.
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README.md Update README.md Jul 10, 2013
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README.md

speedyGApy

SpeedyGApy is a fast, extensible, barebones, vectorized genetic algorithm with uniform crossover, sigma scaling, and stochastic universal sampling. It is a port of the original SpeedyGA from Matlab to Python. SpeedyGApy depends on the Python packages numpy and matplotlib, which can typically be installed by executing

sudo pip install numpy 
sudo pip install matplotlib

If your Python installation doesn't come with pip, you can install it by running sudo easy_install pip.

SpeedyGApy contains two sample fitness functions that serve as examples and also allow users to reproduce the experiments that form the basis for the Hyperclimbing Hypothesis---an explanation for adaptation in genetic algorithms with uniform crossover.

  • The function seapEvolve() uses the the 4-Bit Stochastic Effective Attribute Parity problem discussed here and here, and the visualization capabilities of matplotlib to showcase a computational efficiency of the genetic algorithm. Check for yourself that varying the length of the chromosomes does not affect the expected number of fitness evaluations required for the red dots, marking the locations of the 4 effective attributes, to diverge. Changing the effective attributes, i.e. varying the location of the red dots, also has no effect on the expected time-to-divergence.

  • The function staircaseFunctionEvolve() provides proof of concept that a genetic algorithm is capable of using the computational power showcased by seapEvolve() to implement a global optimization heuristic called Hyperclimbing

SpeedyGA Usage Instructions:

usage: python speedyGA.py [-h] [--fitnessFunction {staircase,seap}]
                   [--probCrossover PROBCROSSOVER]
                   [--probMutation PROBMUTATION] [--popSize POPSIZE]
                   [--bitstringLength BITSTRINGLENGTH] [--gens GENS]

Run SpeedyGA on `seap` or `staircase`, two fitness functions tailored to
provide proof of concept for the Hyperclimbing Hypothesis. More details at
http://blog.hackingevolution.net/2013/01/20/foga-2013-slides/

optional arguments:
  -h, --help            show this help message and exit
  --fitnessFunction {staircase,seap}
                        The fitness function to use (default: staircase).
  --probCrossover PROBCROSSOVER
                        Number between 0 and 1 representing the fraction of
                        the population subject to crossover (default:1)
  --probMutation PROBMUTATION
                        The per bit mutation probability (default:0.005)
  --popSize POPSIZE     Size of the population (default:500)
  --bitstringLength BITSTRINGLENGTH
                        Length of a chromosome in the population (default:500)
  --gens GENS           The number of generations (default:500)

Enjoy!