Exemplo n.º 1
0
        public void AddIntermediateNode(LinkGene a)
        {
            a.Disabled = true;
            int nid = AddNode(NodeType.Intermediate);

            AddConnection(a.Source, nid, RandomWeight());
            AddConnection(nid, a.Destination, RandomWeight());
        }
Exemplo n.º 2
0
        public void AddConnection(int source, int destination, double weight)
        {
            var lg = new LinkGene(source, destination, weight);

            LinkGenotype.Add(lg);
            NodeGenotype[source].Outputs.Add(lg);
            NodeGenotype[destination].Inputs.Add(lg);
        }
Exemplo n.º 3
0
        private void AddConnection(int source, int destination, double weight, int innovation)
        {
            var lg = new LinkGene(source, destination, weight)
            {
                Innovation = innovation
            };

            LinkGenotype.Add(lg);
            NodeGenotype[source].Outputs.Add(lg);
            NodeGenotype[destination].Inputs.Add(lg);
        }
Exemplo n.º 4
0
        public void Mutate()
        {
            // Add Node Mutation
            if (NEATNET.Random.NextDouble() < AddNodeMutationChance)
            {
                // There's a possibility that there aren't any links at all
                if (LinkGenotype.Count > 0)
                {
                    // Pick random link
                    int      ridx = NEATNET.Random.Next(LinkGenotype.Count);
                    LinkGene a    = LinkGenotype[ridx];
                    // Add an intermediate node in the link
                    AddIntermediateNode(a);
                }
            }

            // Add Link Mutation
            if (NEATNET.Random.NextDouble() < AddLinkMutationChance)
            {
                // Pick two unique neurons, there are guaranteed to be at least two
                // Starting neuron cannot be an output neuron, ending neuron cannot be an input neuron
                // Only connect lower nodes to higher nodes (assuming input nodes are lowest, intermediate are sorted by ID, and output are highest)
                // TODO Implementation is flawed, must select two nodes that are UNCONNECTED
                int ridxa = NEATNET.Random.Next(NextNeuronID - OutputNodeCount);
                if (ridxa >= InputNodeCount)
                {
                    ridxa += OutputNodeCount;                          // skip the output nodes
                }
                int ridxb = NEATNET.Random.Next(OutputNodeCount + NextNeuronID - ridxa - 1);
                if (ridxb >= OutputNodeCount)
                {
                    ridxb += ridxa - OutputNodeCount + 1;
                }
                else
                {
                    ridxb += InputNodeCount;
                }
                if (!LinkGeneExists(ridxa, ridxb))
                {
                    AddConnection(ridxa, ridxb, RandomWeight());
                }
            }

            // Link Weight Mutation
            for (int i = 0; i < LinkGenotype.Count; i++)
            {
                var lg = LinkGenotype[i];
                if (NEATNET.Random.NextDouble() < WeightMutationChance)
                {
                    if (NEATNET.Random.NextDouble() < WeightPerturbChance)
                    {
                        // Perturb weighting by a uniform amount
                        lg.Weight += WeightPerturbEpsilon * RandomWeight();
                    }
                    else
                    {
                        // Randomize weighting
                        lg.Weight = RandomWeight();
                    }
                }
            }
            // TODO Implement other mutation types
        }