Reproduction Function

func reproduce(new_agent : Agent, agent1 : Agent, agent2 : Agent):
    randomize()
    var average_fitness = (agent1.fitness + agent2.fitness)/2
    var drastic_mutations : bool = false
    var drastic_percentage_mutations : bool = false
    var mutation_amount : float = 15.0
    var mutation_percentage_amount : float = randf_range(1.0, 2.0)
    if agent1.failed or agent2.failed:
        drastic_mutations = true
        drastic_percentage_mutations = true
    if agent1.worse_than_previous_generation or agent2.worse_than_previous_generation:
        drastic_mutations = true
        drastic_percentage_mutations = true


    if drastic_mutations:
        mutation_amount = randf_range(0.0, 99999.99)
        mutation_percentage_amount = randf_range(1.0, 5.0)
    var x = Node2D.new()
    x.name = "Neurons"
    for i in agent1.neurons.size():
        var n = Neuron.new()
        n.name = "Neuron %d" % [i]
        if randi_range(0, 10) > 5:
            n.weights = agent1.neurons[i].weights
        else:
            n.weights = agent2.neurons[i].weights
        for weight in n.weights:
            if randi_range(0, 100) > 30:
                weight *= randf_range(-mutation_percentage_amount, mutation_percentage_amount)
        if randi_range(0, 10) > 5:
            n.bias = agent1.neurons[i].bias + randf_range(-mutation_amount, mutation_amount)
        else:
            n.bias = agent2.neurons[i].bias + randf_range(-mutation_amount, mutation_amount)
        if randi_range(0, 10) > 5:
            n.threshold = agent1.neurons[i].threshold + randf_range(-mutation_amount, mutation_amount)
        else:
            n.threshold = agent2.neurons[i].threshold + randf_range(-mutation_amount, mutation_amount)
        x.add_child(n)
        new_agent.neurons.append(n)
    new_agent.add_child(x)

    var y = Node2D.new()
    y.name = "Output Neurons"
    for i in agent1.output_neurons.size():
        var n = Neuron.new()
        n.name = "Neuron %d" % [i]
        if randi_range(0, 10) > 5:
            n.weights = agent1.output_neurons[i].weights
        else:
            n.weights = agent2.output_neurons[i].weights
        for weight in n.weights:
            if randi_range(0, 100) > 80:
                weight += randf_range(-mutation_amount, mutation_amount)
        if randi_range(0, 10) > 5:
            n.bias = agent1.output_neurons[i].bias + randf_range(-mutation_amount, mutation_amount)
        else:
            n.bias = agent2.output_neurons[i].bias + randf_range(-mutation_amount, mutation_amount)
        if randi_range(0, 10) > 5:
            n.threshold = agent1.output_neurons[i].threshold + randf_range(-mutation_amount, mutation_amount)
        else:
            n.threshold = agent2.output_neurons[i].threshold + randf_range(-mutation_amount, mutation_amount)
        y.add_child(n)
        new_agent.output_neurons.append(n)
    new_agent.add_child(y)