private void btnFinish_Click(object sender, EventArgs e)
{
populationSize = Convert.ToInt32(numPop.Value);
crossoverProb = Convert.ToDouble(numCross.Value);
mutateProb = Convert.ToDouble(numMutate.Value);
maxGeneration = Convert.ToInt32(numGeneration.Value);
string result = "";
KnapsackGA.Init(items, capacity);
string bestsol = RunGA(populationSize, mutateProb, crossoverProb, maxGeneration, 0.9);
int value = 0;
int weight = 0;
for (int i = 0; i < bestsol.Length; i++)
{
if (bestsol[i] == '1')
{
result += (i + 1) + " ";
value += items[i].v;
weight += items[i].w;
}
}
result += ", Fitness: " + value + ", Weight: " + weight;
lbBestSolution.Text = result;
lbBestSolution.Visible = true;
btnFinish.Enabled = false;
}
public string RunGA(int popSize, double mut, double cross, int maxGen, double percent)
{
List <string> pop = KnapsackGA.generate(popSize);
fitness = KnapsackGA.getFitness(pop);
Stopwatch swObj = new Stopwatch();
swObj.Start();
while (!KnapsackGA.test(fitness, percent) && generation < maxGen)
{
generation++;
pop = KnapsackGA.newPopulation(pop, fitness, mut, cross);
fitness = KnapsackGA.getFitness(pop);
best = KnapsackGA.BestFit(fitness);
}
swObj.Stop();
lbGeneration.Text = generation.ToString();
lbTime.Text = swObj.Elapsed.ToString();
lbTime.Visible = true;
chart1.Visible = true;
RunChart();
dt = new DataTable();
dt.Columns.Add("Chronosome", typeof(string));
dt.Columns.Add("Fitness", typeof(int));
for (int i = 0; i < pop.Count; i++)
{
DataRow row = dt.NewRow();
row["Chronosome"] = pop[i];
row["Fitness"] = fitness[i];
dt.Rows.Add(row);
}
dgvPopulation.DataSource = dt;
return(KnapsackGA.selectElite(pop, fitness));
}