/**
* Template method controlling search. It returns the best individual in the
* specified population, according to the specified FITNESS-FN and goal
* test.
*
* @param initPopulation
* a set of individuals
* @param fitnessFn
* a function that measures the fitness of an individual
* @param goalTest
* test determines whether a given individual is fit enough to
* return. Can be used in subclasses to implement additional
* termination criteria, e.g. maximum number of iterations.
* @param maxTimeMilliseconds
* the maximum time in milliseconds that the algorithm is to run
* for (approximate). Only used if > 0L.
* @return the best individual in the specified population, according to the
* specified FITNESS-FN and goal test.
*/
// function GENETIC-ALGORITHM(population, FITNESS-FN) returns an individual
// inputs: population, a set of individuals
// FITNESS-FN, a function that measures the fitness of an individual
public virtual Individual <A> geneticAlgorithm(ICollection <Individual <A> > initPopulation, IFitnessFunction <A> fitnessFn,
GoalTest <Individual <A> > goalTest, long maxTimeMilliseconds)
{
Individual <A> bestIndividual = null;
// Create a local copy of the population to work with
ICollection <Individual <A> > population = CollectionFactory.CreateQueue <Individual <A> >(initPopulation);
// Validate the population and setup the instrumentation
validatePopulation(population);
updateMetrics(population, 0, 0L);
IStopWatch sw = CommonFactory.CreateStopWatch();
// repeat
int itCount = 0;
do
{
population = nextGeneration(population, fitnessFn);
bestIndividual = retrieveBestIndividual(population, fitnessFn);
updateMetrics(population, ++itCount, sw.GetElapsedMilliseconds());
// until some individual is fit enough, or enough time has elapsed
if (maxTimeMilliseconds > 0L && sw.GetElapsedMilliseconds() > maxTimeMilliseconds)
{
break;
}
if (currIsCancelled)
{
break;
}
} while (!goalTest(bestIndividual));
notifyProgressTrackers(itCount, population);
// return the best individual in population, according to FITNESS-FN
return(bestIndividual);
}
