#region License Information
/* HeuristicLab
* Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
namespace HeuristicLab.Problems.Instances.DataAnalysis {
public class SpatialCoevolution : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Spatial co-evolution F(x,y) = 1/(1+power(x,-4)) + 1/(1+pow(y,-4))"; } }
public override string Description {
get {
return "Paper: Evolutionary consequences of coevolving targets" + Environment.NewLine
+ "Authors: Ludo Pagie and Paulien Hogeweg" + Environment.NewLine
+ "Function: F(x,y) = 1/(1+power(x,-4)) + 1/(1+pow(y,-4))" + Environment.NewLine
+ "Terminal set: x, y" + Environment.NewLine
+ "The fitness of a solution is defined as the mean of the absolute differences between "
+ "the target function and the solution over all problems on the basis of which it is evaluated. "
+ "A solution is considered completely ’correct’ if, for all 676 problems in the ’complete’ "
+ "problem set used in the static evaluation scheme, the absolute difference between "
+ "solution and target function is less than 0:01 (this is a so-called hit).";
}
}
protected override string TargetVariable { get { return "F"; } }
protected override string[] InputVariables { get { return new string[] { "X", "Y", "F" }; } }
protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 1000; } }
protected override int TestPartitionStart { get { return 1000; } }
protected override int TestPartitionEnd { get { return 1676; } }
protected override List> GenerateValues() {
List> data = new List>();
List oneVariableTestData = ValueGenerator.GenerateSteps(-5, 5, 0.4).ToList();
List> testData = new List>() { oneVariableTestData, oneVariableTestData };
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList>();
for (int i = 0; i < AllowedInputVariables.Count(); i++) {
data.Add(ValueGenerator.GenerateUniformDistributedValues(1000, -5, 5).ToList());
data[i].AddRange(combinations[i]);
}
double x, y;
List results = new List();
for (int i = 0; i < data[0].Count; i++) {
x = data[0][i];
y = data[1][i];
results.Add(1 / (1 + Math.Pow(x, -4)) + 1 / (1 + Math.Pow(y, -4)));
}
data.Add(results);
return data;
}
}
}