sarthak247/NEAT-Python

NeuroEvolution of Augmenting Topologies implementation for use in simulations, games, or general machine learning problems

NEAT-Python

NEAT (NeuroEvolution of Augmenting Topologies) is a genetic algorithm developed by Ken Stanley that applies genetic algorithms to machine learning.

1. Generates a population of genomes (neural networks)
2. Groups genomes into species based on their genomic distances
3. Evaluates the fitness score of each genome
4. Breeds and mutates the best genomes over the course of generations

This implementation is a modified version of the algorithm written in Python.

Here is the original paper.

Dependencies

None. This is written in pure Python. :)

Installation

To install via pip, simply enter pip install git+https://github.com/SirBob01/NEAT-Python.git on the command prompt.

Basic Usage

Import the NEAT module.

from neat import neat

Generate the genomic population of a new brain, denoting the number of inputs and outputs respectively, as well as its population count, for its base parameters.

brain = neat.Brain(3, 2, population=100) # Takes 3 inputs, produces 2 outputs
brain.generate()

In evaluating the genomes of a brain, you may use the following format:

while brain.should_evolve():
    genome = brain.get_current()
    output = genome.forward([0.3, 0.1, 0.25])
    
    genome.set_fitness(some_function(output))
    
    brain.next_iteration() # Next genome to be evaluated

The brain's .should_evolve() function determines whether or not to continue evaluating genomes based on the maximum number of generations or fitness score to be achieved.

A genome's .forward() function takes a list of input values and produces a list of output values. These outputs may be evaluated by a fitness function and the fitness score of this current genome may be updated via the genome's .set_fitness() method.

A brain and all its neural networks can be saved to disk and loaded. Files are automatically read and saved as .neat files.

brain.save('filename')
loaded_brain = neat.Brain.load('filename') # Static method

Read NEAT's doc-strings for more information on the module's classes and methods.

TODO

• Modify the mutation probabilities (perhaps allow custom probabilities)
• Fine tune population parameters, such as the delta threshold