public Individual(AlgoDb algoDb)
{
//initialize
this.algoDb = algoDb;
this.invitees = new List <Invitee>(algoDb.invitees);
this.tables = new List <Table>(algoDb.tables);
this.invitessAmount = invitees.Count;
this.tablesAmount = tables.Count;
gens = new Gen[invitessAmount];
/* //generate gens list - all invitess with random tables
* Gen gen = null;
* for (int i = 0; i < gens.Length; i++)
* {
* gen = generateRandomGen(i);
* if(gen != null)
* {
* gens[i] = gen;
* }
* }
*/
for (int i = 0; i < gens.Length; i++)
{
gens[i] = generateRandomGen(i);
}
}
public Individual(Individual copyIndividual)
{
this.algoDb = copyIndividual.algoDb;
cloneGens(copyIndividual.getGens());
this.invitees = copyIndividual.invitees;
this.tables = copyIndividual.tables;
}
public Individual RunAlgo(AlgoDb algoDb)
{
this.algoDb = algoDb;
//Initialize population
Population population = new Population(algoDb);
population.initializePopulation(AlgoConsts.populationLength);
//Calculate fitness of each individual + update Top X
population.CalculateIndividualsFitness();
updateTopX(population);
//While not break condition
while (!breakCondition())
{
//Do selection
parentsCouplesList = Selection(population);
//TODO - population includes individual which doesnt include algoDB! to fix!
//Do crossover - get list of paretns - return new pointer for population
population = CrossOver(parentsCouplesList);
//Do mutation under a random probability
if (algoUtils.AlgoRandom(1000) < 5)
{
population.runMutation();
}
//Calculate new fitness value + update top X
population.CalculateIndividualsFitness();
updateTopX(population);
iterations++;
}
return(GetIndividualWithBestResult());
}
public Population(AlgoDb algoDb)
{
this.algoDb = algoDb;
this.invitees = new List <Invitee>(algoDb.invitees);
this.tables = new List <Table>(algoDb.tables);
}
public Population(Individual[] individuals, AlgoDb algoDb)
{
cloneIndividuals(individuals);
this.algoDb = algoDb;
}
public Individual(int gensSize, AlgoDb algoDb)
{
this.algoDb = algoDb;
gens = new Gen[gensSize];
}
