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Some sample C# code on how a genetic algorithm can be applied to the linear assignment problem. This problem can be efficiently solved using the Hungarian algorithm, but I wanted to demonstrate how the genetic algorithm can produce an optimal solution too. This example is implemented as a simple console application using code developed in Microsoft Visual Studio. To help break down the problem a little, I develop a number of classes for the purposes of encoding potential solutions as chromosomes, maintaining a population of chromosome and applying genetic operators to the population of solutions in the form of crossover, mutation and selection. Code samples as follows: Program.cs The main program loop: [code language="csharp"] using System; namespace LinearAssignmentProblem { class Program { static void Main(string[] args) { var tasks = 5; var popSize = 100; var rnd = new Random(); // Do we seek to maxi...

From StackOverflow searches, I see that there are two possible ways: use an implementation of a TrulyObservableCollection; or use a Binding List. StackOverflow resource: https://stackoverflow.com/questions/1427471/observablecollection-not-noticing-when-item-in-it-changes-even-with-inotifyprop In your console application first implement a property that implements INotifyPropertyChanged for you: ViewModelBase.cs [code language="csharp"] using System.ComponentModel; namespace ObservableCollection { public class ViewModelBase : INotifyPropertyChanged { public event PropertyChangedEventHandler PropertyChanged; protected void OnPropertyChanged(string propertyName) { var handler = PropertyChanged; handler?.Invoke(this, new PropertyChangedEventArgs(propertyName)); } } } [/code] Code samples as follows: 1. TrulyObservableCollection [code language="csharp"] using System; using System.Collections.Generic...