StaticNeuroGenetic

This library use a genetic algorithm to fit a neural network weights. 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.

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
    }

    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(
    []int{2, 3, 1},                     //Neural network's layers [input, hiddens..., output]
    staticneurogenetic.Sigmoid,         //Activation function for the neural network
    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,                                //MutRate (probability to mutate a new individual)
    0.1,                                //MutSize (how big the mutation will be)
    staticneurogenetic.OneMutation,     //MutType
    staticneurogenetic.DivPointCross,   //CrossType
)

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

for i := 0; i < 300; i++ {
    agents.ResetFitness() //Set all fitness to 0, for use AddFitness
    evalAll(agents)
    agents.NextGeneration() //Evolve each neural networks
}

Example

You can find a complete example here