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source: branches/2965_CancelablePersistence/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Various/SpatialCoevolution.cs @ 16439

Last change on this file since 16439 was 15583, checked in by swagner, 7 years ago

#2640: Updated year of copyrights in license headers

File size: 4.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Random;
27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
29  public class SpatialCoevolution : ArtificialRegressionDataDescriptor {
30
31    public override string Name { get { return "Spatial co-evolution F(x,y) = 1/(1 + x^(-4)) + 1/(1 + y^(-4))"; } }
32    public override string Description {
33      get {
34        return "Paper: Evolutionary consequences of coevolving targets" + Environment.NewLine
35        + "Authors: Ludo Pagie and Paulien Hogeweg" + Environment.NewLine
36        + "Function: F(x,y) = 1/(1 + x^(-4)) + 1/(1 + y^(-4))" + Environment.NewLine
37        + "Non-terminals: +, -, *, % (protected division), sin, cos, exp, ln(|x|) (protected log)" + Environment.NewLine
38        + "Terminals: only variables (no random constants)" + Environment.NewLine
39        + "The fitness of a solution is defined as the mean of the absolute differences between "
40        + "the target function and the solution over all problems on the basis of which it is evaluated. "
41        + "A solution is considered completely ’correct’ if, for all 676 problems in the ’complete’ "
42        + "problem set used in the static evaluation scheme, the absolute difference between "
43        + "solution and target function is less than 0.01 (this is a so-called hit).";
44      }
45    }
46    protected override string TargetVariable { get { return "F"; } }
47    protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
48    protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
49    protected override int TrainingPartitionStart { get { return 0; } }
50    protected override int TrainingPartitionEnd { get { return 676; } }
51    protected override int TestPartitionStart { get { return 676; } }
52    protected override int TestPartitionEnd { get { return 1676; } }
53    public int Seed { get; private set; }
54
55    public SpatialCoevolution() : this((int)DateTime.Now.Ticks) { }
56
57    public SpatialCoevolution(int seed) : base() {
58      Seed = seed;
59    }
60    protected override List<List<double>> GenerateValues() {
61      List<List<double>> data = new List<List<double>>();
62
63      List<double> evenlySpacedSequence = SequenceGenerator.GenerateSteps(-5, 5, 0.4m).Select(v => (double)v).ToList();
64      List<List<double>> trainingData = new List<List<double>>() { evenlySpacedSequence, evenlySpacedSequence };
65      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList();
66      var rand = new MersenneTwister((uint)Seed);
67
68      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
69        data.Add(combinations[i].ToList());
70        data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(rand.Next(), 1000, -5, 5).ToList());
71      }
72
73      double x, y;
74      List<double> results = new List<double>();
75      for (int i = 0; i < data[0].Count; i++) {
76        x = data[0][i];
77        y = data[1][i];
78        results.Add(1 / (1 + Math.Pow(x, -4)) + 1 / (1 + Math.Pow(y, -4)));
79      }
80      data.Add(results);
81
82      return data;
83    }
84  }
85}
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