private void CrossOver(ICollection <Creature> creatures, Bounds bounds)
{
var amountCrossOver = (int)(creatures.Count * CrossOverChance / 100);
for (var i = 0; i < amountCrossOver; ++i)
{
var father = Selection();
var mother = Selection();
var fatherWeights = father.Brain.GetWeights();
var motherWeights = mother.Brain.GetWeights();
var childWeights = new double[fatherWeights.Length];
var crossOverPoint = Random.Range(0, fatherWeights.Length);
for (var k = 0; k < childWeights.Length; ++k)
{
childWeights[k] = k < crossOverPoint
? fatherWeights[k]
: motherWeights[k];
}
var child = Prefabs.CreateCreature().SpawnIn(bounds, Generation);
child.Brain.SetWeights(childWeights);
NextGeneration.Add(child);
}
}
public List <Creature> SpawnInitialPopulation(int amount, Bounds bounds)
{
NextGeneration = new List <Creature>(amount);
for (var i = 0; i < amount; ++i)
{
NextGeneration.Add(Prefabs.CreateCreature().SpawnIn(bounds, Generation));
}
Generation++;
return(NextGeneration);
}
private void SpawnInitialPopulationAndFoodSupply()
{
// spawn initial population
_creatures = GeneticAlgorythm.SpawnInitialPopulation(InitialPopulation, Bounds);
// spawn initial food supply
_foodSupply = new List <Food>(InitialFoodSupply);
for (var i = 0; i < InitialFoodSupply; ++i)
{
var food = Prefabs.CreateFood();
food.SpawnIn(Bounds);
_foodSupply.Add(food);
}
name = "World [" + _creatures.Count + "]";
}
