source: branches/2521_ProblemRefactoring/HeuristicLab.Problems.Programmable/3.3/Templates/MultiObjectiveProblem_Template.cs @ 17225

using System;
using System.Linq;
using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using ENCODING_NAMESPACE;
using HeuristicLab.Optimization;
using HeuristicLab.Problems.Programmable;

namespace HeuristicLab.Problems.Programmable {
  public class CompiledMultiObjectiveProblemDefinition : CompiledMultiObjectiveProblemDefinition<ENCODING_CLASS, SOLUTION_CLASS> {
    public override bool[] Maximization { get { return new[] { false, false }; } }
    public override double[] ReferencePoint { get { return null; } }
    public override IReadOnlyList<double[]> BestKnownFront { get { return null; } }

    public override void Initialize() {
      // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
      // Define e.g. the length of the solution encoding or the solution creator by modifying the Encoding property
      // Encoding.Length = 100;
      // Add additional initialization code e.g. private variables that you need for evaluating
    }

    public override double[] Evaluate(SOLUTION_CLASS solution, IRandom random) {
      // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
      var quality = new[] { 0.0, 0.0 };
      return quality;
    }

    public override void Analyze(SOLUTION_CLASS[] solutions, 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
    }

    public override IEnumerable<SOLUTION_CLASS> GetNeighbors(SOLUTION_CLASS solution, 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 = (SOLUTION_CLASS)solution.Clone();
        // modify the solution specified as neighbor
        yield return neighbor;
      }
    }

    // Implement further classes and methods
  }
}