Gene[] GetGenes(int sum, Dictionary <int, List <Gene> > proportionalPopulation)
{
var result = new Gene[2];
for (int i = 0; i < 2; i++)
{
if (i == 0)
{
result[i] = RouletteWheelSelection(sum, proportionalPopulation);
}
else
{
result[i] = RouletteWheelSelection(sum, proportionalPopulation, result[0]);
}
}
return(result);
}
/// <summary>
/// Returns the offspring genes resulting from a single point crossover chosen at random
/// </summary>
public Gene[] SinglePointCrossover(Gene parent1, Gene parent2)
{
var crossoverPosition = UnityEngine.Random.Range(0, _geneSize);
var offspring1 = new List <Action>();
var offspring2 = new List <Action>();
for (int chromosome = 0; chromosome < _geneSize; chromosome++)
{
var p1 = parent1[chromosome];
var p2 = parent2[chromosome];
var swap = crossoverPosition >= chromosome;
if (swap)
{
offspring2.Add(p1);
offspring1.Add(p2);
}
else
{
offspring1.Add(p1);
offspring2.Add(p2);
}
}
var child1 = new Gene(offspring1);
var child2 = new Gene(offspring2);
if (!child1.IsValid() || !child2.IsValid())
{
return(SinglePointCrossover(parent1, parent2));
}
return(new Gene[]
{
child1,
child2
});
}
/// <summary>
/// Returns 2 offsprings using Uniform UniformCrossover, by default using 0.5 ratio
/// </summary>
public Gene[] UniformCrossover(Gene parent1, Gene parent2, float crossoverRatio = 0.5f)
{
var offspring1 = new List <Action>();
var offspring2 = new List <Action>();
for (int chromosome = 0; chromosome < _geneSize; chromosome++)
{
var p1 = parent1[chromosome];
var p2 = parent2[chromosome];
var swap = Utility.CheckProbability(crossoverRatio);
if (swap)
{
offspring2.Add(p1);
offspring1.Add(p2);
}
else
{
offspring1.Add(p1);
offspring2.Add(p2);
}
}
var child1 = new Gene(offspring1);
var child2 = new Gene(offspring2);
if (!child1.IsValid() || !child2.IsValid())
{
return(UniformCrossover(parent1, parent2, crossoverRatio));
}
return(new Gene[]
{
child1,
child2
});
}
protected bool Equals(Gene other)
{
return(FitnessValue.Equals(other.FitnessValue) && ActionsExecuted == other.ActionsExecuted);
}
public Gene(Gene other)
{
ActionsExecuted = other.ActionsExecuted;
FitnessValue = other.FitnessValue;
AddRange(other.ToList());
}
/// <summary>
/// Selects a gene using roulette wheel selection
/// </summary>
Gene RouletteWheelSelection(int sum, Dictionary <int, List <Gene> > proportionalPopulation, Gene except = null)
{
var roll = Utility.GetRandomInt(0, sum);
var previous = 0;
foreach (var rank in proportionalPopulation)
{
if (roll >= previous && roll <= rank.Key + previous)
{
var random = Utility.SelectRandom(rank.Value);
return(random.Equals(except) ? RouletteWheelSelection(sum, proportionalPopulation, except) : random);
}
previous += rank.Key;
}
return(null);
}
