Generating "Hello World" with a genetic algorithm in Python
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# Genetic Hello World

## Introduction
  Uses an genetic algorithm to go from a list of random strings to our target.

  1. Creates a list of random strings with generate_random_chromosomes(),
  called 'samples.'

  2. Selects a subset of the samples to create a 'selected' group. First takes
  the top 1% samples (elitism), then uses tourny select to find the
  rest. Tourny select works by selecting two random samples, and using the
  one with the highest fitness score.

  3. Creates a 'solution' group by randomly taking two chromosomes from the
  selected group and breeding (combining) these together. Periodically, a
  mutiation factor is added to this new 'solution.' Repeat this process until
  we have a sufficent number of solutions.

  4. Repeat steps 2 and 3, with each generated solution set becoming the new
  generation of samples. Do this until a generation has a solution of fitness
  of 0 (matches our target).

## Usage
  import genetic_hello_world

  # Set your parameters (taken from __init__ defaults)
  target = "Hello World!"  # Target string we're going for
  num_samples = 1000       # Have 1000 chromos in the sample group
  num_selected = 100       # Have 100 chromos in the selected group
  mutation_factor = 10     # Mutate every 10 chromosomes
  ghw = genetic_hello_world.GeneticHelloWorld(
    target, num_samples, num_selected, mutation_factor)
  # Run the script until the target is found

## Changelog 
  v1.02: Bug fix that makes sure we get 10% of our selected set from elitism
    and 90% from tournament selection.
  v1.01: Made a GeneticHelloWorld class that allows the user to customize the 
    program using parameters to __init__. Uses same defaults as before.
  v1.00: Initial release.

## Inspiration
  Idea and algorithm inspired completely by Howard Yeend's blog post 
  at .
  I have also created a Clojure version, available at .

## License
  Copyright Topher Brown <>, 2010. Released under the MIT