[11753] | 1 | using System;
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| 2 | using System.Linq;
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| 3 | using System.Collections.Generic;
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| 4 | using HeuristicLab.Common;
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| 5 | using HeuristicLab.Core;
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| 6 | using HeuristicLab.Data;
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[11949] | 7 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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| 8 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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[12731] | 9 | using HeuristicLab.Encodings.RealVectorEncoding;
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[11753] | 10 | using HeuristicLab.Encodings.PermutationEncoding;
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[12731] | 11 | using HeuristicLab.Encodings.LinearLinkageEncoding;
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[12724] | 12 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[11753] | 13 | using HeuristicLab.Optimization;
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| 14 | using HeuristicLab.Problems.Programmable;
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| 15 |
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| 16 | namespace HeuristicLab.Problems.Programmable {
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| 17 | public class CompiledSingleObjectiveProblemDefinition : CompiledProblemDefinition, ISingleObjectiveProblemDefinition {
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| 18 | public bool Maximization { get { return false; } }
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| 19 |
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| 20 | public override void Initialize() {
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[11880] | 21 | // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
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[11753] | 22 | // Define the solution encoding which can also consist of multiple vectors, examples below
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[11900] | 23 | //Encoding = new BinaryVectorEncoding("b", length: 5);
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| 24 | //Encoding = new IntegerVectorEncoding("i", length: 5, min: 2, max: 14, step: 2);
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| 25 | //Encoding = new RealVectorEncoding("r", length: 5, min: -1.0, max: 1.0);
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[11753] | 26 | //Encoding = new PermutationEncoding("p", length: 5, type: PermutationTypes.Absolute);
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[12724] | 27 | //Encoding = new LinearLinkageEncoding("l", length: 5);
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| 28 | //Encoding = new SymbolicExpressionTreeEncoding("s", new SimpleSymbolicExpressionGrammar(), 50, 12);
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[11880] | 29 | // The encoding can also be a combination
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[11753] | 30 | //Encoding = new MultiEncoding()
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[11900] | 31 | //.Add(new BinaryVectorEncoding("b", length: 5))
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| 32 | //.Add(new IntegerVectorEncoding("i", length: 5, min: 2, max: 14, step: 4))
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| 33 | //.Add(new RealVectorEncoding("r", length: 5, min: -1.0, max: 1.0))
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[11753] | 34 | //.Add(new PermutationEncoding("p", length: 5, type: PermutationTypes.Absolute))
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[12731] | 35 | //.Add(new LinearLinkageEncoding("l", length: 5))
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| 36 | //.Add(new SymbolicExpressionTreeEncoding("s", new SimpleSymbolicExpressionGrammar(), 50, 12))
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[11880] | 37 | ;
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| 38 | // Add additional initialization code e.g. private variables that you need for evaluating
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[11753] | 39 | }
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| 40 |
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| 41 | public double Evaluate(Individual individual, IRandom random) {
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[11880] | 42 | // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
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[11753] | 43 | var quality = 0.0;
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| 44 | //quality = individual.RealVector("r").Sum(x => x * x);
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| 45 | return quality;
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| 46 | }
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| 47 |
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[11880] | 48 | public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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| 49 | // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
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| 50 | // Write or update results given the range of vectors and resulting qualities
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| 51 | // Uncomment the following lines if you want to retrieve the best individual
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[12001] | 52 |
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| 53 | //var orderedIndividuals = individuals.Zip(qualities, (i, q) => new { Individual = i, Quality = q }).OrderBy(z => z.Quality);
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| 54 | //var best = Maximization ? orderedIndividuals.Last().Individual : orderedIndividuals.First().Individual;
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| 55 |
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| 56 | //if (!results.ContainsKey("Best Solution")) {
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| 57 | // results.Add(new Result("Best Solution", typeof(RealVector)));
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| 58 | //}
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| 59 | //results["Best Solution"].Value = (IItem)best.RealVector("r").Clone();
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[11753] | 60 | }
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| 61 |
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| 62 | public IEnumerable<Individual> GetNeighbors(Individual individual, IRandom random) {
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[11880] | 63 | // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
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[11753] | 64 | // Create new vectors, based on the given one that represent small changes
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[11880] | 65 | // This method is only called from move-based algorithms (Local Search, Simulated Annealing, etc.)
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[11753] | 66 | while (true) {
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[11880] | 67 | // Algorithm will draw only a finite amount of samples
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| 68 | // Change to a for-loop to return a concrete amount of neighbors
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[11753] | 69 | var neighbor = individual.Copy();
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[11880] | 70 | // For instance, perform a single bit-flip in a binary parameter
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[11753] | 71 | //var bIndex = random.Next(neighbor.BinaryVector("b").Length);
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| 72 | //neighbor.BinaryVector("b")[bIndex] = !neighbor.BinaryVector("b")[bIndex];
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| 73 | yield return neighbor;
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| 74 | }
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| 75 | }
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| 76 |
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[11880] | 77 | // Implement further classes and methods
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[11753] | 78 | }
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| 79 | }
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| 80 |
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