/// <summary>
/// Creates a new solver.
/// </summary>
/// <param name="population_size"></param>
/// <param name="stagnation"></param>
/// <param name="generation_operation"></param>
/// <param name="cross_over_operation"></param>
public EdgeAssemblyCrossOverSolver(int population_size, int stagnation,
IGenerationOperation<List<int>, GeneticProblem, Fitness> generation_operation,
ICrossOverOperation<List<int>, GeneticProblem, Fitness> cross_over_operation)
{
//_stopped = false;
_stagnation = stagnation;
_population_size = population_size;
_generation_operation = generation_operation;
_cross_over_operation = cross_over_operation;
}
/// <summary>
/// Creates a new solver.
/// </summary>
/// <param name="population_size"></param>
/// <param name="stagnation"></param>
/// <param name="generation_operation"></param>
/// <param name="cross_over_operation"></param>
public EdgeAssemblyCrossOverSolver(int population_size, int stagnation,
IGenerationOperation <List <int>, GeneticProblem, Fitness> generation_operation,
ICrossOverOperation <List <int>, GeneticProblem, Fitness> cross_over_operation)
{
//_stopped = false;
_stagnation = stagnation;
_population_size = population_size;
_generation_operation = generation_operation;
_cross_over_operation = cross_over_operation;
}
/// <summary>
/// Creates a genetic solver.
/// </summary>
public GeneticSolver()
{
_mutation_operation = new BestDetailedPlacementMutationOperation();
_cross_over_operation = new SequentialContructiveCrossoverOperator();
_generation_operation = new BestPlacementGenerationOperation();
_eltism = 10;
_mutation = 30;
_cross = 20;
_population = 20;
_stagnation_count = 100;
}
/// <summary>
/// Creates a genetic solver.
/// </summary>
public GeneticSolver()
{
_mutation_operation = new BestDetailedPlacementMutationOperation();
_cross_over_operation = new SequentialContructiveCrossoverOperator();
_generation_operation = new BestPlacementGenerationOperation();
_eltism = 10;
_mutation = 30;
_cross = 20;
_population = 20;
_stagnation_count = 100;
}
/// <summary>
/// Creates a new solver.
/// </summary>
/// <param name="settings"></param>
/// <param name="selector"></param>
/// <param name="combined_operation"></param>
/// <param name="problem"></param>
/// <param name="fitness_calculator"></param>
public Solver(
ProblemType problem,
SolverSettings settings,
ISelector <GenomeType, ProblemType, WeightType> selector,
IOperation <GenomeType, ProblemType, WeightType> combined_operation,
IFitnessCalculator <GenomeType, ProblemType, WeightType> fitness_calculator)
{
_problem = problem;
_settings = settings;
_selector = selector;
_mutation_operation = combined_operation;
_crossOverOperation = combined_operation;
_generation_operation = combined_operation;
_fitness_calculator = fitness_calculator;
}
/// <summary>
/// Creates a genetic solver.
/// </summary>
/// <param name="population"></param>
/// <param name="stagnation"></param>
/// <param name="elitism"></param>
/// <param name="mutation_operation"></param>
/// <param name="mutation"></param>
/// <param name="cross_over_operation"></param>
/// <param name="cross_over"></param>
/// <param name="generation_operation"></param>
public GeneticSolver(int population, int stagnation, int elitism,
IMutationOperation <List <int>, GeneticProblem, Fitness> mutation_operation, int mutation,
ICrossOverOperation <List <int>, GeneticProblem, Fitness> cross_over_operation, int cross_over,
IGenerationOperation <List <int>, GeneticProblem, Fitness> generation_operation)
{
_mutation_operation = mutation_operation;
_cross_over_operation = cross_over_operation;
_generation_operation = generation_operation;
_eltism = elitism;
_mutation = mutation;
_cross = cross_over;
_population = population;
_stagnation_count = stagnation;
}
/// <summary>
/// Creates a new solver.
/// </summary>
/// <param name="problem"></param>
/// <param name="settings"></param>
/// <param name="selector"></param>
/// <param name="mutation"></param>
/// <param name="cross_over"></param>
/// <param name="generation"></param>
/// <param name="fitness_calculator"></param>
/// <param name="accept_only_better_cross_over"></param>
/// <param name="accept_only_better_mutation"></param>
public Solver(
ProblemType problem,
SolverSettings settings,
ISelector <GenomeType, ProblemType, WeightType> selector,
IMutationOperation <GenomeType, ProblemType, WeightType> mutation,
ICrossOverOperation <GenomeType, ProblemType, WeightType> cross_over,
IGenerationOperation <GenomeType, ProblemType, WeightType> generation,
IFitnessCalculator <GenomeType, ProblemType, WeightType> fitness_calculator,
bool accept_only_better_cross_over, bool accept_only_better_mutation)
{
_problem = problem;
_settings = settings;
_selector = selector;
_generation_operation = generation;
_mutation_operation = mutation;
_crossOverOperation = cross_over;
_fitness_calculator = fitness_calculator;
_accept_only_better_on_cross_over = accept_only_better_cross_over;
_accept_only_better_on_mutation = accept_only_better_mutation;
}
/// <summary>
/// Creates a genetic solver.
/// </summary>
/// <param name="population"></param>
/// <param name="stagnation"></param>
/// <param name="elitism"></param>
/// <param name="mutation_operation"></param>
/// <param name="mutation"></param>
/// <param name="cross_over_operation"></param>
/// <param name="cross_over"></param>
/// <param name="generation_operation"></param>
public GeneticSolver(int population, int stagnation, int elitism,
IMutationOperation<List<int>, GeneticProblem, Fitness> mutation_operation, int mutation,
ICrossOverOperation<List<int>, GeneticProblem, Fitness> cross_over_operation, int cross_over,
IGenerationOperation<List<int>, GeneticProblem, Fitness> generation_operation)
{
_mutation_operation = mutation_operation;
_cross_over_operation = cross_over_operation;
_generation_operation = generation_operation;
_eltism = elitism;
_mutation = mutation;
_cross = cross_over;
_population = population;
_stagnation_count = stagnation;
}
/// <summary>
/// Start the solver with an initial population.
/// </summary>
/// <param name="initial"></param>
public Individual <GenomeType, ProblemType, WeightType> Start(Population <GenomeType, ProblemType, WeightType> initial)
{
WeightType fitness = default(WeightType);
Population <GenomeType, ProblemType, WeightType> population = new Population <GenomeType, ProblemType, WeightType>(initial, true);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"Generating population...");
while (population.Count < _settings.PopulationSize)
{
// generate new.
IGenerationOperation <GenomeType, ProblemType, WeightType> generation_operation = this.CreateGenerationOperation();
Individual <GenomeType, ProblemType, WeightType> new_individual =
generation_operation.Generate(this);
// add to population.
population.Add(new_individual);
// report population generation.
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"Generating population {0}/{1}...", population.Count, _settings.PopulationSize);
this.ReportNew(string.Format("Generating population..."), population.Count, _settings.PopulationSize);
}
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.SolversSolver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"Done!");
// sort the initial population.
population.Sort(this, _fitness_calculator);
//Tools.Core.Output.OutputTextStreamHost.WriteLine("Average {0}",
// _fitness_calculator.AverageFitness(_problem, population));
int stagnation = 0;
// start next generation or stop.
WeightType new_fitness =
population[0].Fitness;
// is there improvement?
if (new_fitness.CompareTo(fitness) > 0)
{ // a new fitness is found!
// raise a new event here.
this.RaiseNewFittest(population[0]);
// set new fitness.
fitness = new_fitness;
fittest = population[0];
// reset the stagnation count.
stagnation = 0;
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"New Fittest {0}", fittest.ToString());
}
// start getting genetic!
int generation_count = 0;
while (generation_count < _settings.MaxGeneration &&
stagnation < _settings.StagnationCount)
{
// check if the solving has been cancelled.
if (this.Cancelled)
{
this.ReportNew(string.Format("Solver cancelled at generation {0}!", generation_count), 1, 1);
return(null);
}
if (this.Stopped)
{
this.ReportNew(string.Format("Solver stopped at generation {0}!", generation_count), 1, 1);
return(fittest);
}
// calculate the next generation.
population =
this.NextGeneration(population);
// get the population fitness.
population.Sort(this, _fitness_calculator);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"{0}->{1}", population[0].Fitness, population[population.Count - 1].Fitness);
// start next generation or stop.
new_fitness =
population[0].Fitness;
// is there improvement?
if (new_fitness.CompareTo(fitness) < 0)
{ // a new fitness is found!
// raise a new event here.
this.RaiseNewFittest(population[0]);
// set new fitness.
fitness = new_fitness;
fittest = population[0];
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"New Fittest {0}-{1} {2}", generation_count, stagnation, fittest.ToString());
// reset the stagnation count.
stagnation = 0;
// report the new fittest.
this.ReportNew(string.Format("New Fittest {0}",
fittest.ToString()), 0, 1);
}
else
{
// increase the stagnation count.
stagnation++;
}
// raise the generation count.
generation_count++;
// raise a new generation event.
this.RaiseNewGeneration(generation_count, stagnation, population);
// report the new generation.
this.ReportNew(string.Format("Generation {0}-{1} {2}",
generation_count,
stagnation,
fittest.ToString()), stagnation, _settings.StagnationCount);
if (stagnation != 0 && stagnation % 1 == 0)
{
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"Generation {0}-{1} {2}", generation_count, stagnation, fittest.ToString());
}
}
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"Result [{0}]:", fitness, fittest.ToString());
// report the new generation.
this.ReportNew(string.Format("Evolution finished @ generation {0}: {1}",
generation_count,
fittest.ToString()), generation_count, _settings.StagnationCount);
OsmSharp.Logging.Log.TraceEvent("OsmSharp.Math.AI.Genetic.Solvers.Solver<GenomeType, ProblemType, WeightType>", TraceEventType.Information,
"Evolution finished @ generation {0}: {1}", generation_count, fittest.ToString());
return(fittest);
}