NEAT

This is a neuro-evolution of augmenting topologies library. It uses a genetic algorithm to evolve neural networks.

This is useful when you don't have a dataset to train your neural network, for example when you need an agent to interact with an environment or to learn to play some games.

It's similar to StaticNeuroGenetic library, but this can also evolve the topology of the neural network.

How to use

Create an evaluation function

// Eval an individual
func eval(agents *staticneurogenetic.SNG, individual int) {
    inputs := [][]float64 {
        []float64 {0, 0},
        []float64 {0, 1},
        []float64 {1, 0},
        []float64 {1, 1},
    }
    targets := []float64 {
        1,
        0,
        0,
        1
    }

    agents.SetFitness(individual, 0)
    for i, input := range inputs {
        // Get individual output ([]float64)
        output := agents.Output(individual, input)
        // Calculate how wrong is the output
        dif := math.abs(targets[i] - output[0])
        // Added to the fitness
        agents.AddFitness(individual, 1 - dif)
    }
}

// Eval each individual
func evalAll(agents *staticneurogenetic.SNG) {
    for i := range agents.Population {
        eval(agents, i)
    }
}

Create a new set of agents

agents := staticneurogenetic.NewSNG(
    2,                              //Input size
    1,                              //Output size
    300,                            //PopulationSize (number of individual to work with)
    10,                             //Survivors (number of individual that will not change in next generation and to use as parents)
    0.1,                            //Probability to mutate a new individual
    0.1,                            //Maximun size of mutations
    0.2,                            //Probability to change bias
    0.2,                            //Probability to add new neurons
    neat.GetActivation("sigmoid"),  //Activation function for the neural network
    false,                          //Not recurrent
)

To train the agents we just need to get the next generation

for i := 0; i < 300; i++ {
    evalAll(agents)
    agents.NextGeneration() //Evolve each neural networks
}