public override AbstractPhenotype Translate(BitVector genom)
{
//NB: Requires explicit size genom size of 33
double a = binaryToDoubleRange(genom.GetRange(0, 7), 0.001, 0.2); //200 values : 8 bit
double b = binaryToDoubleRange(genom.GetRange(7, 7), 0.01, 0.3); // 30 values : 5 bit
double c = binaryToDoubleRange(genom.GetRange(14, 7), -80.0, -30.0); // 51 values : 6 bit
double d = binaryToDoubleRange(genom.GetRange(21, 7), 0.1, 10.0); // 100 values : 7 bit
double k = binaryToDoubleRange(genom.GetRange(28, 7), 0.01, 1.0); // 100 values : 7 bit
return new IzhikevichPhenotype(a, b, c, d, k) {Genotype = genom, Fitness = 0.0, RouletteProportion = 0.0};
}
public override AbstractPhenotype Translate(BitVector genom)
{
Network network = new Network();
network.createGraph();
int index = 0;
foreach (Node hiddenNode in network.HiddenNodes)
{
foreach (var neighbour in hiddenNode.UpstreamConnections)
{
if (neighbour.left == network.BiasNode)
neighbour.right = binaryToDoubleRange(genom.GetRange(index++*8, 8), -10.0, 0.0);
else
neighbour.right = binaryToDoubleRange(genom.GetRange(index++*8, 8), -5.0, 5.0);
}
hiddenNode.Gain = binaryToDoubleRange(genom.GetRange(index++*8, 8), 1.0, 5.0);
hiddenNode.TimeConstant = binaryToDoubleRange(genom.GetRange(index++*8, 8), 1.0, 2.0);
}
foreach (Node outputNode in network.OutputNodes)
{
foreach (var neighbour in outputNode.UpstreamConnections)
{
if (neighbour.left == network.BiasNode)
neighbour.right = binaryToDoubleRange(genom.GetRange(index++*8, 8), -10.0, 0.0);
else
neighbour.right = binaryToDoubleRange(genom.GetRange(index++*8, 8), -5.0, 5.0);
}
outputNode.Gain = binaryToDoubleRange(genom.GetRange(index++*8, 8), 1.0, 5.0);
outputNode.TimeConstant = binaryToDoubleRange(genom.GetRange(index++*8, 8), 1.0, 2.0);
}
MinCogPhenotype pheno = new MinCogPhenotype(network.InputNodes, network.HiddenNodes, network.OutputNodes, network.BiasNode)
{
Genotype = genom,
Fitness = 0.0,
RouletteProportion = 0.0
};
return pheno;
}