This implementation is based on K.O. Stanley and R. Miikkulainen's paper. http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
NEAT stands for Neural Evolution of Augmenting Topologies. Just as our brain uses neurons to interpret information, this algorithm searches spaces minimally by creating new connections competitively until a solution is found.
- If you're creating a Swift executable, you can simply add the package to your app.
To use NEAT-swift inside of an XCode project:
import Neat
- Create a NEAT network.
let network = Neat(inputs: 2, outputs: 1, population: 300, confFile: nil, multithread: false)
NOTE: "confFile" and "multithread" are not fully functional. "confFile" is not functional mostly because of the inability to edit mutation percentages. "multithread" is functional, but only for 8 threads (i.e. most quad-core processors).
- Support for other thread amounts will be coming soon. For now, 1 thread or 8 threads are supported.
Typical implementation for a single thread NEAT network (network to be tested until a solution is found or close to it).
let input = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
let expected = [[0.0], [1.0], [1.0], [0.0]]
let fitnessGoal = 16.0
var generation = 1
var king: NGenome?
while true {
for _ in 1...network.populationSize {//testing entire population
var sumedTotal = 0.0
// Test the Genome Pool
for i in 0..<input.count {
let output = network.run(inputs: input[i], inputCount: inputs, outputCount: outputs)
for o in 0..<output.count {
sumedTotal += abs(expected[i][o] - output[o])
}
}
// Assign genome a fitness score from the test.
let currentGenomeFitness = pow(sqrt(fitnessGoal) - sumedTotal, 2)
// conditions
if currentGenomeFitness > HighestFitness { HighestFitness = currentGenomeFitness }
// Next
network.nextGenome(currentGenomeFitness)
}
// Do NEAT here.
network.epoch()
king = network.getKing()
print(network.description)
print("Generation: \(generation)")
generation += 1
if king!.fitness >= fitnessGoal*0.99 {
// Print out the description after the solution is found only.
// Printing out the description before will result in an unpredictable outcome
print(king!.description)
break
}
} //end
let fitness = network.testNetwork(genome: king!, inputs: input, expected: expected, inputCount: inputs, outputCount: outputs, testType: .distanceSquared, info: true)
print("Fitness: \(fitness)")