Genetic solver written in Python

import genetic

best = genetic.getBest(get_fitness, display, minLen, optimalFitness,
        geneSet=None, createGene=None, maxLen=None,
        customMutate=None, customCrossover=None)

an Individual with attributes:

• Genes: list of genes
• Fitness: fitness value for those genes
• Strategy: strategy that produced the gene sequence (mutate, crossover, or random)

• get_fitness: takes a list of genes and returns an integer value representing how close that particular candidate comes to the optimal solution. Higher values are better.
• display: takes an Individual. Called to provide visual output of better candidates as they are discovered.
• minLen: minimum valid length for an Individual's genes.
• maxLen: optional maximum valid length for an Individual's genes. Defaults to minLen.
• optimalFitness: expected fitness value for the best solution.
• geneSet: optional list of gene values for generating new gene sequences. If not provided, createGene must be provided.
• createGene: optional function to create a random gene. Called with the gene sequence index into which it will be placed, and the length of that gene sequence. If not provided, geneSet must be provided.
• customMutate: optional replacement for the built in mutate function. Called with the new child genes. Changes should be made to the child genes.
• customCrossover: optional replacement for the built in crossover function. Called with the new child genes, and a copy of a second parent's genes. Changes should be made to the child genes.

• stringDuplicationTests.py - duplicates a string, see related blog post

• 8queensTests.py - solves the 8 Queens Puzzle, see related blog post

• graphColoringTests.py - 4-coloring maps, see related blog post

The following sample projects included in this repository work but no longer have a related blog post because I've changed my implementation approach.

• equationGenerationTests.py - equation discovery

• operationGenerationTests.py - generating OR and XOR with only AND and NOT

The code and examples in ths repository work as is and you are welcome to use them. In late 2015 I restarted from scratch with a slower feature progression to resolve some performance issues with more advanced genetic programming topics. That has evolved into an e-book entitled Genetic Algorithms with Python that I'm writing on Leanpub. Try the sample chapter.

MIT License