private void UpdateGrammar()
{
var g = new SimpleSymbolicExpressionGrammar();
g.AddSymbols(new[] { "AND", "OR" }, 2, 2); // See Koza 1992, page 171, section 7.4.1 11-multiplexer
g.AddSymbols(new[] { "NOT" }, 1, 1);
g.AddSymbols(new[] { "IF" }, 3, 3);
// find the number of address lines and input lines
// e.g. 11-MUX: 3 addrBits + 8 input bits
var addrBits = (int)Math.Log(NumberOfBits, 2); // largest power of two that fits into the number of bits
var inputBits = NumberOfBits - addrBits;
for (int i = 0; i < addrBits; i++)
{
g.AddTerminalSymbol(string.Format("a{0}", i));
}
for (int i = 0; i < inputBits; i++)
{
g.AddTerminalSymbol(string.Format("d{0}", i));
}
Encoding.Grammar = g;
BestKnownQuality = Math.Pow(2, NumberOfBits); // this is a benchmark problem (the best achievable quality is known for a given number of bits)
}
public Problem()
: base() {
BoolMatrix world = new BoolMatrix(ToBoolMatrix(santaFeAntTrail));
Parameters.Add(new ValueParameter<BoolMatrix>("World", "The world for the artificial ant with scattered food items.", world));
Parameters.Add(new ValueParameter<IntValue>("MaximumTimeSteps", "The number of time steps the artificial ant has available to collect all food items.", new IntValue(600)));
base.BestKnownQuality = 89;
var g = new SimpleSymbolicExpressionGrammar();
g.AddSymbols(new string[] { "IfFoodAhead", "Prog2" }, 2, 2);
g.AddSymbols(new string[] { "Prog3" }, 3, 3);
g.AddTerminalSymbols(new string[] { "Move", "Left", "Right" });
base.Encoding = new SymbolicExpressionTreeEncoding(g, 20, 10);
base.Encoding.GrammarParameter.ReadOnly = true;
}
private void UpdateGrammar()
{
// whenever ProblemData is changed we create a new grammar with the necessary symbols
var g = new SimpleSymbolicExpressionGrammar();
g.AddSymbols(new[] { "+", "*", "%", "-" }, 2, 2); // % is protected division 1/0 := 0
foreach (var variableName in ProblemData.AllowedInputVariables)
{
g.AddTerminalSymbol(variableName);
}
// generate ephemeral random consts in the range [-10..+10[ (2*number of variables)
var rand = new System.Random();
for (int i = 0; i < ProblemData.AllowedInputVariables.Count() * 2; i++)
{
string newErcSy;
do
{
newErcSy = string.Format("{0:F2}", rand.NextDouble() * 20 - 10);
} while (g.Symbols.Any(sy => sy.Name == newErcSy)); // it might happen that we generate the same constant twice
g.AddTerminalSymbol(newErcSy);
}
Encoding.Grammar = g;
}
public GaussianProcessCovarianceOptimizationProblem()
: base()
{
Parameters.Add(new ValueParameter <IRegressionProblemData>(ProblemDataParameterName, "The data for the regression problem", new RegressionProblemData()));
Parameters.Add(new FixedValueParameter <IntValue>(ConstantOptIterationsParameterName, "Number of optimization steps for hyperparameter values", new IntValue(50)));
Parameters.Add(new FixedValueParameter <IntValue>(RestartsParameterName, "The number of random restarts for constant optimization.", new IntValue(10)));
Parameters["Restarts"].Hidden = true;
var g = new SimpleSymbolicExpressionGrammar();
g.AddSymbols(new string[] { "Sum", "Product" }, 2, 2);
g.AddTerminalSymbols(new string[]
{
"Linear",
"LinearArd",
"MaternIso1",
"MaternIso3",
"MaternIso5",
"NeuralNetwork",
"Periodic",
"PiecewisePolynomial0",
"PiecewisePolynomial1",
"PiecewisePolynomial2",
"PiecewisePolynomial3",
"Polynomial2",
"Polynomial3",
"RationalQuadraticArd",
"RationalQuadraticIso",
"SpectralMixture1",
"SpectralMixture3",
"SpectralMixture5",
"SquaredExponentialArd",
"SquaredExponentialIso"
});
base.Encoding = new SymbolicExpressionTreeEncoding(g, 10, 5);
}
public Problem()
: base()
{
Parameters.Add(new FixedValueParameter <IntValue>(LawnWidthParameterName, "Width of the lawn.", new IntValue(8)));
Parameters.Add(new FixedValueParameter <IntValue>(LawnLengthParameterName, "Length of the lawn.", new IntValue(8)));
var g = new SimpleSymbolicExpressionGrammar();
g.AddSymbols(new string[] { "Sum", "Prog" }, 2, 2);
g.AddSymbols(new string[] { "Frog" }, 1, 1);
g.AddTerminalSymbols(new string[] { "Left", "Forward" });
// initialize 20 ephemeral random constants in [0..32[
var fastRand = new FastRandom(314159);
for (int i = 0; i < 20; i++)
{
g.AddTerminalSymbol(string.Format("{0},{1}", fastRand.Next(0, 32), fastRand.Next(0, 32)));
}
Encoding = new SymbolicExpressionTreeEncoding(g, 1000, 17);
}
private void UpdateGrammar()
{
var g = new SimpleSymbolicExpressionGrammar();
g.AddSymbols(new[] { "AND", "OR", "NAND", "NOR" }, 2, 2); // see Koza, 1992, page 529 section 20.2 Symbolic Regression of Even-Parity Functions
// add one terminal symbol for each bit
for (int i = 0; i < NumberOfBits; i++)
{
g.AddTerminalSymbol(string.Format("{0}", i));
}
Encoding.Grammar = g;
BestKnownQuality = Math.Pow(2, NumberOfBits); // this is a benchmark problem (the best achievable quality is known for a given number of bits)
}