Neuroevolution and NEAT
Neuroevolution is a fantastic area that belongs to artificial intelligence. Neuroevolution is about evolving neural networks to solve a particular problem. Neuroevolution differs from classical deep learning in the way a satisfactory model is obtained. Classical learning techniques for Deep Learning (e.g., backpropagation) are about learning, while Neuroevolution is about evolving a model.
NEAT is maybe the most popular neuroevolution algorithm. A description of NEAT may be found in the paper Evolving Neural Networks through Augmenting Topologies.
The content of this GitHub repository is heavily inspired from the book Agile Artificial Intelligence in Pharo: Implementing Neural Networks, Genetic Algorithms, and Neuroevolution.
This repository provides an implementation of NEAT for the Pharo programming language. Many implementations of NEAT exist in a wide range of programming languages. The advantages of NEAT4Pharo is to have a relativaly small amount of source code (< 1000 LOC), and it offers interactive visualization to give a better understanding of how the evolution was carried out.
Execute the following script to load the Roassal2 visualization engine and NEAT4Pharo:
Metacello new baseline: 'Roassal2'; repository: 'github://ObjectProfile/Roassal2/src'; load. Gofer new url: 'github://bergel/NEAT/src'; package: 'NEAT'; load.
One of the introductory example in neural network, is to build a neural network that expresses the XOR logical gate. We can do so using NEAT. Consider the following script:
neat := NEAT new. neat numberOfInputs: 2. neat numberOfOutputs: 1. neat fitness: [ :ind | | score | score := 0. #(#(0 0 0) #(0 1 1) #(1 0 1) #(1 1 0)) do: [ :tuple | diff := (ind evaluate: (tuple first: 2)) first - tuple last. score := score + (diff * diff) ]. (score / -4) asFloat ]. neat numberOfGenerations: 200. neat run
The script configure the NEAT algorithm to handles individual (i.e., neural networks) having two inputs and one output. The XOR logical gates takes two arguments and return one value. So, a neural network with 2 inputs and 1 output is sufficent to express the XOR.
We see the curve of the maximum fitness reaches 0. This means that the NEAT algorithm was able to produce through evolution a neural network that express the XOR logical gate. We can veriy this:
neat result evaluate: #(0 0). "Return #(0.0024744051266554854)" neat result evaluate: #(0 1). "#(0.9992445715215523)" neat result evaluate: #(1 0). "#(0.9901246518281834)" neat result evaluate: #(1 1). "#(0.006270828175993032)"
In addition to the fitness curve, inspecting the object
neat gives additional relevant visualizations. For example, the tab
#Species gives the evolution of the number of species during the generations:
We see that the number of species increases significantly at the begining of the algorithm execution to reach a relatively sable value around 25 species. Clicking on a dot opens the species visualization:
The species visualization represents species. The size of a box is the size of the species, i.e., the number of individual that belongs to the species. The color fading indicate the fitness value of the best individual. Clicking on a species list the individuals that belongs to the species. Clicking on an individual open a visualization of the neural network
Wanna to chat about it?
Join the Pharo discord server and join the
#ia channel. You are also very welcome to post issues to this GitHub repository.