using System; using System.Linq; using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.BinaryVectorEncoding; using HeuristicLab.Encodings.IntegerVectorEncoding; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Encodings.PermutationEncoding; using HeuristicLab.Encodings.LinearLinkageEncoding; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Optimization; using HeuristicLab.Problems.Programmable; namespace HeuristicLab.Problems.Programmable { public class CompiledSingleObjectiveProblemDefinition : CompiledProblemDefinition, ISingleObjectiveProblemDefinition { public bool Maximization { get { return false; } } public override void Initialize() { // Use vars.yourVariable to access variables in the variable store i.e. yourVariable // Define the solution encoding which can also consist of multiple vectors, examples below //Encoding = new BinaryVectorEncoding("b", length: 5); //Encoding = new IntegerVectorEncoding("i", length: 5, min: 2, max: 14, step: 2); //Encoding = new RealVectorEncoding("r", length: 5, min: -1.0, max: 1.0); //Encoding = new PermutationEncoding("p", length: 5, type: PermutationTypes.Absolute); //Encoding = new LinearLinkageEncoding("l", length: 5); //Encoding = new SymbolicExpressionTreeEncoding("s", new SimpleSymbolicExpressionGrammar(), 50, 12); // The encoding can also be a combination //Encoding = new MultiEncoding() //.Add(new BinaryVectorEncoding("b", length: 5)) //.Add(new IntegerVectorEncoding("i", length: 5, min: 2, max: 14, step: 4)) //.Add(new RealVectorEncoding("r", length: 5, min: -1.0, max: 1.0)) //.Add(new PermutationEncoding("p", length: 5, type: PermutationTypes.Absolute)) //.Add(new LinearLinkageEncoding("l", length: 5)) //.Add(new SymbolicExpressionTreeEncoding("s", new SimpleSymbolicExpressionGrammar(), 50, 12)) ; // Add additional initialization code e.g. private variables that you need for evaluating } public double Evaluate(Individual individual, IRandom random) { // Use vars.yourVariable to access variables in the variable store i.e. yourVariable var quality = 0.0; //quality = individual.RealVector("r").Sum(x => x * x); return quality; } public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) { // Use vars.yourVariable to access variables in the variable store i.e. yourVariable // Write or update results given the range of vectors and resulting qualities // Uncomment the following lines if you want to retrieve the best individual //var orderedIndividuals = individuals.Zip(qualities, (i, q) => new { Individual = i, Quality = q }).OrderBy(z => z.Quality); //var best = Maximization ? orderedIndividuals.Last().Individual : orderedIndividuals.First().Individual; //if (!results.ContainsKey("Best Solution")) { // results.Add(new Result("Best Solution", typeof(RealVector))); //} //results["Best Solution"].Value = (IItem)best.RealVector("r").Clone(); } public IEnumerable GetNeighbors(Individual individual, IRandom random) { // Use vars.yourVariable to access variables in the variable store i.e. yourVariable // Create new vectors, based on the given one that represent small changes // This method is only called from move-based algorithms (Local Search, Simulated Annealing, etc.) while (true) { // Algorithm will draw only a finite amount of samples // Change to a for-loop to return a concrete amount of neighbors var neighbor = individual.Copy(); // For instance, perform a single bit-flip in a binary parameter //var bIndex = random.Next(neighbor.BinaryVector("b").Length); //neighbor.BinaryVector("b")[bIndex] = !neighbor.BinaryVector("b")[bIndex]; yield return neighbor; } } // Implement further classes and methods } }