// Use the temp population to get the next gerneration using crossover and partner selection
public static List <Organism> generateNextGen(List <Organism> gen)
{
List <int> gene = new List <int>();
List <int> crossPoitns = new List <int>();
List <Organism> nextGen = new List <Organism>();
if (elitism)
{
Organism elite = new Organism(GENES);
foreach (Organism org in gen)
{
if (org.fitness + org.numberOfdecptives > elite.fitness + elite.numberOfdecptives)
{
elite = org;
}
}
nextGen.Add(elite);
}
//Cross over first
for (var pop = nextGen.Count; pop < POPULATION; pop++)
{
// Get best partner for organism
var parentB = getPartner(gen, pop);
nextGen.Add(new Organism(GENES));
if (!weightedCrossover)
{
while (crossPoitns.Count < numOfCrossPoints)
{
var point = GetRandomNumber(0, GENES);
if (!crossPoitns.Contains(point))
{
crossPoitns.Add(point);
}
}
}
else if (weightedCrossover && numOfCrossPoints == 1)
{
float fitnessA = gen[pop].fitness + (gen[pop].numberOfdecptives * 10);
float fitnessB = gen[parentB].fitness + (gen[parentB].numberOfdecptives * 10);
float com = fitnessA + fitnessB;
float cross = (fitnessA / com);
cross = cross * GENES;
crossPoitns.Add((int)cross);
}
crossPoitns.Add(0);
crossPoitns.Sort();
// This is the cross over algorithm uses random cross point.
// Test use of different number of cross points
// Maybe add weighted cross over or min differenc between cross points
// Add ability for multiple cross over.
// Should cross points change for each cross over or stay the same for each generation
var endPoint = 0;
var parent = 0;
if (!uniformCrossover && !halfUniformCrossover)
{
for (var b = 0; b < crossPoitns.Count; b++)
{
endPoint = b + 1 < crossPoitns.Count ? crossPoitns[b + 1] : GENES;
if (parent == 0)
{
gene = gen[pop].spliceGenes(crossPoitns[b], endPoint);
nextGen[pop].addGenes(gene, crossPoitns[b], endPoint);
parent = 1;
}
else if (parent == 1)
{
gene = gen[parentB].spliceGenes(crossPoitns[b], endPoint);
nextGen[pop].addGenes(gene, crossPoitns[b], endPoint);
parent = 0;
}
}
nextGen[pop] = mutate(nextGen[pop]);
crossPoitns.Clear();
}
else if (uniformCrossover)
{
var end = 0;
var start = 0;
for (var g = 0; g < ALLELES; g++)
{
var rand = GetRandomNumber(1, 10);
if (rand < 5)
{
end += alleleSizes[g];
var genes = gen[parent].spliceGenes(start, end);
nextGen[pop].addGenes(genes, start, end);
}
else
{
end += alleleSizes[g];
var genes = gen[parentB].spliceGenes(start, end);
nextGen[pop].addGenes(genes, start, end);
}
start = end;
}
nextGen[pop].fitness = 0;
nextGen[pop].numberOfdecptives = 0;
nextGen[pop] = mutate(nextGen[pop]);
crossPoitns.Clear();
}
else if (halfUniformCrossover)
{
var toSwap = getHammingDistance(gen[pop], gen[parentB]) / 2;
nextGen[pop].genes = gen[pop].genes;
var swaped = 0;
List <int> changed = new List <int>();
while (swaped != toSwap)
{
var rand = GetRandomNumber(0, GENES - 1);
if (!changed.Contains(rand))
{
changed.Add(rand);
if (gen[pop].genes[rand] != gen[parentB].genes[rand])
{
nextGen[pop].genes.SetValue(gen[parentB].genes[rand], rand);
swaped++;
}
}
}
nextGen[pop].fitness = 0;
nextGen[pop].numberOfdecptives = 0;
nextGen[pop] = mutate(nextGen[pop]);
crossPoitns.Clear();
}
}
if (!useEpistasis)
{
checkForIdeal(nextGen);
}
return(nextGen);
}
