#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.Programmable {
[Item("Single-objective Script", "Script that defines the parameter vector and evaluates the solution for a programmable problem.")]
[StorableClass]
public class SingleObjectiveScript : ProgrammableProblemScript, IStorableContent {
public string Filename { get; set; }
[StorableConstructor]
protected SingleObjectiveScript(bool deserializing) : base(deserializing) { }
protected SingleObjectiveScript(SingleObjectiveScript original, Cloner cloner)
: base(original, cloner) { }
public SingleObjectiveScript() {
Code = CodeTemplate;
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SingleObjectiveScript(this, cloner);
}
public new ISingleObjectiveProblemDefinition Instance {
get { return (ISingleObjectiveProblemDefinition)base.Instance; }
protected set { base.Instance = value; }
}
protected override string CodeTemplate {
get {
return @"using System;
using System.Linq;
using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Problems.Programmable;
public class ProblemDefinition : ISingleObjectiveProblemDefinition {
public ProblemDefinition() {
// initialize private fields
}
public bool IsMaximizationProblem { get { return false; } }
public Configuration GetConfiguration() {
return new Configuration()
// .AddBinary(""b"", length: 5)
// .AddInteger(""i"", length: 5, min: 2, max: 14, step: 4)
// .AddReal(""r"", length: 5, min: -1.0, max: 1.0)
// .AddPermutation(""P"", length: 5, type: PermutationTypes.Absolute)
;
}
public double Evaluate(IRandom random, ParameterVector vector) {
var quality = 0.0;
// quality = vector.Real(""r"").Select(x => x * x).Sum();
return quality;
}
public IEnumerable GetNeighbors(IRandom random, ParameterVector vector) {
// Create new vectors, based on the given one that represent small changes
// This method is only called from move-based algorithms (LocalSearch, SimulatedAnnealing, etc.)
while (true) {
var neighbor = (ParameterVector)vector.Clone();
//e.g. make a bit flip in a binary parameter
//var bIndex = random.Next(neighbor.Binary(""b"").Length);
//neighbor.Binary(""b"")[bIndex] = !neighbor.Binary(""b"")[bIndex];
yield return neighbor;
}
}
// implement further classes and methods
}";
}
}
}
}