public static double EvaluateFitness(Chromosome chromosome)
{
double fitnessValue = 0;
//decode chromosome
Bag bag = new Bag();
for (int i = 0; i < chromosome.Count; i++)
{
if (chromosome.Genes[i].BinaryValue == 1)
{
bag.AddItem(knapsackItems[i]);
}
}
if (bag.TotalWeight <= 400)
{
//put fitnessValue between 0 and 1
fitnessValue = (bag.TotalValue / Convert.ToDouble(10000));
//Console.WriteLine(" ----------------------- Total Weight : {0} Total Value : {1} Fitness : {2} ", bag.TotalWeight, bag.TotalWeight, fitnessValue.ToString());
}
return(fitnessValue);
}
private static void ga_OnGenerationComplete(object sender, GaEventArgs e)
{
// lay giai phap tot nhat
var chromosome = e.Population.GetTop(1)[0];
//decode chromosome
Bag bag = new Bag();
for (int i = 0; i < chromosome.Count; i++)
{
if (chromosome.Genes[i].BinaryValue == 1)
{
bag.AddItem(knapsackItems[i]);
}
}
Console.WriteLine(" Generation info: Total Weight : {0} Total Value : {1} Fitness : {2}", bag.TotalWeight, bag.TotalValue, e.Population.MaximumFitness);
}
public static double EvaluateFitness(Chromosome chromosome)
{
double fitnessValue = 0;
//decode chromosome
Bag bag = new Bag();
for (int i = 0; i < chromosome.Count; i++)
{
if (chromosome.Genes[i].BinaryValue == 1)
{
bag.AddItem(knapsackItems[i]);
}
}
if (bag.TotalWeight <= 400)
{
fitnessValue = (bag.TotalValue / Convert.ToDouble(10000));
}
return(fitnessValue);
}
static void Main(string[] args)
{
knapsackItems = new List <Bag.Item>();
knapsackItems.Add(new Bag.Item()
{
Name = "Map", Weight = 9, Value = 150
});
knapsackItems.Add(new Bag.Item()
{
Name = "Water", Weight = 153, Value = 200
});
knapsackItems.Add(new Bag.Item()
{
Name = "Compass", Weight = 13, Value = 35
});
knapsackItems.Add(new Bag.Item()
{
Name = "Sandwitch", Weight = 50, Value = 160
});
knapsackItems.Add(new Bag.Item()
{
Name = "Glucose", Weight = 15, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "Tin", Weight = 68, Value = 45
});
knapsackItems.Add(new Bag.Item()
{
Name = "Banana", Weight = 27, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "Apple", Weight = 39, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "Cheese", Weight = 23, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "Beer", Weight = 52, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "Suntan Cream", Weight = 11, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "Camera", Weight = 32, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "T-shirt", Weight = 24, Value = 15
});
knapsackItems.Add(new Bag.Item()
{
Name = "Trousers", Weight = 48, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "Umbrella", Weight = 73, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "WaterProof Trousers", Weight = 42, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "Note-Case", Weight = 22, Value = 80
});
knapsackItems.Add(new Bag.Item()
{
Name = "Sunglasses", Weight = 7, Value = 20
});
knapsackItems.Add(new Bag.Item()
{
Name = "Towel", Weight = 18, Value = 12
});
knapsackItems.Add(new Bag.Item()
{
Name = "Socks", Weight = 4, Value = 50
});
knapsackItems.Add(new Bag.Item()
{
Name = "Book", Weight = 30, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "waterproof overclothes ", Weight = 43, Value = 75
});
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
//create the population
var population = new Population(100, 22, false, false);
//create the genetic operators
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
//create the GA itself
var ga = new GeneticAlgorithm(population, EvaluateFitness);
//subscribe to the GAs Generation Complete event
ga.OnGenerationComplete += ga_OnGenerationComplete;
//add the operators to the ga process pipeline
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
//run the GA
ga.Run(TerminateAlgorithm);
//get the best solution and print Items
var Bestchromosome = ga.Population.GetTop(1)[0];
//decode chromosome
Console.WriteLine("Maximum value of knapsack contains these Items : ");
Bag BestBag = new Bag();
for (int i = 0; i < Bestchromosome.Count; i++)
{
if (Bestchromosome.Genes[i].BinaryValue == 1)
{
Console.WriteLine(knapsackItems[i].ToString());
BestBag.AddItem(knapsackItems[i]);
}
}
Console.WriteLine(" Best knapsack information:\n Total Weight : {0} Total Value : {1} Fitness : {2}", BestBag.TotalWeight, BestBag.TotalValue, Bestchromosome.Fitness);
Console.ReadKey();
}
static void Main(string[] args)
{
knapsackItems = new List <Bag.Item>();
knapsackItems.Add(new Bag.Item()
{
Name = "a", Weight = 9, Value = 150
});
knapsackItems.Add(new Bag.Item()
{
Name = "b", Weight = 153, Value = 200
});
knapsackItems.Add(new Bag.Item()
{
Name = "c", Weight = 13, Value = 35
});
knapsackItems.Add(new Bag.Item()
{
Name = "d", Weight = 50, Value = 160
});
knapsackItems.Add(new Bag.Item()
{
Name = "e", Weight = 15, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "f", Weight = 68, Value = 45
});
knapsackItems.Add(new Bag.Item()
{
Name = "g", Weight = 27, Value = 60
});
knapsackItems.Add(new Bag.Item()
{
Name = "h", Weight = 39, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "i", Weight = 23, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "j", Weight = 52, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "k", Weight = 11, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "l", Weight = 32, Value = 30
});
knapsackItems.Add(new Bag.Item()
{
Name = "m", Weight = 24, Value = 15
});
knapsackItems.Add(new Bag.Item()
{
Name = "n", Weight = 48, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "o", Weight = 73, Value = 40
});
knapsackItems.Add(new Bag.Item()
{
Name = "p", Weight = 42, Value = 70
});
knapsackItems.Add(new Bag.Item()
{
Name = "q", Weight = 22, Value = 80
});
knapsackItems.Add(new Bag.Item()
{
Name = "r", Weight = 7, Value = 20
});
knapsackItems.Add(new Bag.Item()
{
Name = "s", Weight = 18, Value = 12
});
knapsackItems.Add(new Bag.Item()
{
Name = "t", Weight = 4, Value = 50
});
knapsackItems.Add(new Bag.Item()
{
Name = "u", Weight = 30, Value = 10
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
knapsackItems.Add(new Bag.Item()
{
Name = "v", Weight = 43, Value = 75
});
const double crossoverProbability = 0.65;
const double mutationProbability = 0.08;
const int elitismPercentage = 5;
// tao quan the
var population = new Population(100, 22, false, false);
// tao toan tu di truyen
var elite = new Elite(elitismPercentage);
var crossover = new Crossover(crossoverProbability, true)
{
CrossoverType = CrossoverType.SinglePoint
};
var mutation = new BinaryMutate(mutationProbability, true);
// tao GA
var ga = new GeneticAlgorithm(population, EvaluateFitness);
ga.OnGenerationComplete += ga_OnGenerationComplete;
// them tung toan tu vao GA
ga.Operators.Add(elite);
ga.Operators.Add(crossover);
ga.Operators.Add(mutation);
// chay GA
ga.Run(TerminateAlgorithm);
// lay do vat tot nhat
var Bestchromosome = ga.Population.GetTop(1)[0];
// decode chromosome
Console.WriteLine("Maximum value of knapsack contains these Items : ");
Bag BestBag = new Bag();
for (int i = 0; i < Bestchromosome.Count; i++)
{
if (Bestchromosome.Genes[i].BinaryValue == 1)
{
Console.WriteLine(knapsackItems[i].ToString());
BestBag.AddItem(knapsackItems[i]);
}
}
Console.WriteLine(" Best knapsack information:\n Total Weight : {0} Total Value : {1} Fitness : {2}", BestBag.TotalWeight, BestBag.TotalValue, Bestchromosome.Fitness);
Console.ReadKey();
}