private static void RunGeneticOptimizer()
{
//optimizer variables
var variables = new List <GeneticOptimizerVariable>()
{
new GeneticOptimizerVariable("x", 4, -10, 10)
};
//optimizer configuration
var configuration = new GeneticOptimizerConfiguration("y", variables, 1);
//objective function
var objectiveFunction = new Func <double[], double>(inputs =>
{
Console.WriteLine("objectiveFunction");
Thread.Sleep(1000);
var objectiveFunctionResult = Math.Pow(inputs[0], 3) / Math.Exp(Math.Pow(inputs[0], 0.8));
return(objectiveFunctionResult);
});
//optimizer
var optimizer = new GeneticOptimizer(configuration, objectiveFunction, GenerationRanCallback);
var watch = new Stopwatch();
watch.Start();
optimizer.Start();
watch.Stop();
Console.WriteLine($"Number milliseconds: {watch.ElapsedMilliseconds}");
Console.WriteLine($"Press Key to quit");
Console.ReadLine();
}
public GeneticOptimizer(GeneticOptimizerConfiguration configuration, Func <double[], double> objectiveFunction, Action <string> generationRanCallback = null)
{
//store configuration
_configuration = configuration;
_generationRanCallback = generationRanCallback;
//set min/max/precision of input variables
var minValues = new double[_configuration.Variables.Count];
var maxValues = new double[_configuration.Variables.Count];
var fractionDigits = new int[_configuration.Variables.Count];
for (int index = 0; index < _configuration.Variables.Count; index++)
{
minValues[index] = _configuration.Variables[index].MinimumValue;
maxValues[index] = _configuration.Variables[index].MaximumValue;
fractionDigits[index] = _configuration.Variables[index].NumberDigitsPrecision;
}
//total bits
var totalBits = new int[] { 64 };
//chromosome
var chromosome = new FloatingPointChromosome(minValues, maxValues, totalBits, fractionDigits);
//population
var population = new Population(MinimumNumberPopulation, MaximumNumberPopulation, chromosome);
//set fitness function
var fitnessFunction = new FuncFitness(c =>
{
var fc = c as FloatingPointChromosome;
var inputs = fc.ToFloatingPoints();
var result = objectiveFunction(inputs);
//add to results
if (!Double.IsNaN(result))
{
var list = inputs.ToList();
list.Add(result);
_result.IterationArray.Add(string.Join(",", list));
}
return(result);
});
//other variables
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new FitnessThresholdTermination();
_algorithm = new GeneticAlgorithm(population, fitnessFunction, selection, crossover, mutation)
{
Termination = termination,
};
//task parallelism
var taskExecutor = new ParallelTaskExecutor();
taskExecutor.MinThreads = 1;
taskExecutor.MaxThreads = _configuration.NumberThreadsToUse;
_algorithm.TaskExecutor = taskExecutor;
//if (_configuration.NumberThreadsToUse > 1)
//{
// var taskExecutor = new ParallelTaskExecutor();
// taskExecutor.MinThreads = 1;
// taskExecutor.MaxThreads = _configuration.NumberThreadsToUse;
// _algorithm.TaskExecutor = taskExecutor;
//}
//register generation ran callback
_algorithm.GenerationRan += AlgorithmOnGenerationRan;
}
