static void Main(string[] args)
{
var pop = new List <IIndividual>(32);
Console.WriteLine("Read from file individuals?(y/n)");
var yayornay = Console.ReadLine();
if (yayornay == "y")
{
Console.WriteLine("File path");
var path = Console.ReadLine();
string [] strArr = null;
try{
strArr = File.ReadAllLines(path);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
Console.ReadKey(false);
Environment.Exit(1);
}
foreach (var str in strArr)
{
Console.WriteLine("Read " + str);
if (str.Trim() == string.Empty)
{
continue;
}
pop.Add(new GotchaIndividual(str));
}
}
else
{
Console.WriteLine("Creating individuals");
for (var ii = 0; ii < 32; ++ii)
{
pop.Add(new GotchaIndividual());
}
}
Ages myGA = new Ages(
1000,
new CompareEvaluate(Evaluate),
GotchaIndividual.GarboCrossoverOperator,
(r) => new GotchaIndividual(),
pop);
myGA.SetRandomSeed(0);
myGA.GoThroughGenerations();
Console.ReadKey();
}
private void Test_PolynomialGA(Func <double, double> func)
{
//Settings
bool adaptive = false;
int seed = 0;
Utils.SetRandomSeed(seed);
var rng = new Random(seed);
PolynomialOrder = 5;
int populationSize = 100;
//number of samples to use the in the polynomial approx
// to test the differnce
double start = -10, end = 10;
//Step between the samples
double step = 0.02;
//Number of generations to bundle
int genBundleCount = 10;
//Stop at Gen
GenerationStop = 500;
//Get Data for tests
double[] xRange, expectedValues;
GetFofXForRange(func, start, step, end, out xRange, out expectedValues);
double[][] allPowsOfX = GetPowersOfX(xRange, PolynomialOrder);
ExpectedValsX = xRange;
ExpectedValsY = expectedValues;
PowsOfXForAllX = allPowsOfX;
//Create Delegates
Evaluate eval = (i) =>
((CartesianIndividual)i).PolynomialEval(allPowsOfX, expectedValues);
CrossOver crossOver = adaptive
? AdaptiveCartesianIndividual.CrossOver
: (CrossOver)CartesianIndividual.CrossOver;
Generate generate = adaptive
? (Generate)((r) => new AdaptiveCartesianIndividual(PolynomialOrder, 1, r: r, emptySigma: true))
: (r) => new CartesianIndividual(PolynomialOrder, 1, r);
// Generate Population
List <IIndividual> pop;
pop = Enumerable
.Range(0, populationSize)
.Select(i => generate(rng))
.Cast <IIndividual>()
.ToList();
Ages = new Ages(genBundleCount,
eval,
crossOver,
generate,
pop);
Ages.SetRandomSeed(seed);
Ages.NicheStrat = new NicheDensityStrategy(
Ages,
(l, r) => CartesianIndividual.Distance((CartesianIndividual)l, (CartesianIndividual)r));
}