#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 System;
using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.Programmable {
[Item("Single-objective Problem Definition Script", "Script that defines the parameter vector and evaluates the solution for a programmable problem.")]
[StorableClass]
public sealed class SingleObjectiveProblemDefinitionScript : ProblemDefinitionScript, ISingleObjectiveProblemDefinition, IStorableContent {
public string Filename { get; set; }
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.Optimization;
using HeuristicLab.Problems.Programmable;
public class CustomProblemDefinition : CompiledProblemDefinition, ISingleObjectiveProblemDefinition {
public bool IsMaximizationProblem { get { return false; } }
public CustomProblemDefinition() {
// Define the solution encoding which can also consist of multiple vectors, examples below
// Encoding = new BinaryEncoding(""b"", length: 5);
// Encoding = new IntegerEncoding(""i"", lenght: 5, min: 2, max: 14, step: 4);
// Encoding = new RealEncoding(""r"", length: 5, min: -1.0, max: 1.0);
// Encoding = new PermutationEncoding(""P"", length: 5, type: PermutationTypes.Absolute);
// Encoding = new MultiEncoding()
// .AddBinaryVector(""b"", length: 5)
// .AddIntegerVector(""i"", length: 5, min: 2, max: 14, step: 4)
// .AddRealVector(""r"", length: 5, min: -1.0, max: 1.0)
// .AddPermutation(""P"", length: 5, type: PermutationTypes.Absolute)
;
}
public override void Initialize() {
// when the definition is created here you can initialize variables in the variable store
}
public double Evaluate(IRandom random, Individual individual) {
var quality = 0.0;
// use vars.yourVariable to access variables in the variable store i.e. yourVariable
// quality = individual.RealVector(""r"").Sum(x => x * x);
return quality;
}
public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results) {
// write or update results given the range of vectors and resulting qualities
// use e.g. vars.yourVariable to access variables in the variable store i.e. yourVariable
}
public override IEnumerable GetNeighbors(IRandom random, Individual individual) {
// 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) {
// this is not an infinite loop as only a finite amount of samples will be drawn
// it is possible to return a concrete amount of neighbors also
var neighbor = (Individual)individual.Clone();
//e.g. make a bit flip in a binary parameter
//var bIndex = random.Next(neighbor.BinaryVector(""b"").Length);
//neighbor.BinaryVector(""b"")[bIndex] = !neighbor.BinaryVector(""b"")[bIndex];
yield return neighbor;
}
}
// implement further classes and methods
}";
}
}
[StorableConstructor]
private SingleObjectiveProblemDefinitionScript(bool deserializing) : base(deserializing) { }
private SingleObjectiveProblemDefinitionScript(SingleObjectiveProblemDefinitionScript original, Cloner cloner) : base(original, cloner) { }
public SingleObjectiveProblemDefinitionScript() :base(){
Code = CodeTemplate;
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SingleObjectiveProblemDefinitionScript(this, cloner);
}
public new ISingleObjectiveProblemDefinition CompiledProblemDefinition {
get { return (ISingleObjectiveProblemDefinition)base.CompiledProblemDefinition; }
}
bool ISingleObjectiveProblemDefinition.Maximization {
get { return CompiledProblemDefinition != null && CompiledProblemDefinition.Maximization; }
}
double ISingleObjectiveProblemDefinition.Evaluate(Individual individual, IRandom random) {
return CompiledProblemDefinition.Evaluate(individual, random);
}
void ISingleObjectiveProblemDefinition.Analyze(Individual[] individuals, double[] qualities, ResultCollection results) {
CompiledProblemDefinition.Analyze(individuals, qualities, results);
}
}
}