#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Optimization { [StorableClass] public abstract class MultiObjectiveBasicProblem : BasicProblem, IMultiObjectiveHeuristicOptimizationProblem, IMultiObjectiveProblemDefinition where TEncoding : class, IEncoding { [StorableConstructor] protected MultiObjectiveBasicProblem(bool deserializing) : base(deserializing) { } protected MultiObjectiveBasicProblem(MultiObjectiveBasicProblem original, Cloner cloner) : base(original, cloner) { ParameterizeOperators(); } protected MultiObjectiveBasicProblem() : base() { Parameters.Add(new ValueParameter("Maximization", "Set to false if the problem should be minimized.", (BoolArray)new BoolArray(Maximization).AsReadOnly())); Operators.Add(Evaluator); Operators.Add(new MultiObjectiveAnalyzer()); ParameterizeOperators(); } [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { ParameterizeOperators(); } public abstract bool[] Maximization { get; } public abstract double[] Evaluate(Individual individual, IRandom random); public virtual void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results, IRandom random) { } protected override void OnOperatorsChanged() { base.OnOperatorsChanged(); if (Encoding != null) { PruneSingleObjectiveOperators(Encoding); var multiEncoding = Encoding as MultiEncoding; if (multiEncoding != null) { foreach (var encoding in multiEncoding.Encodings.ToList()) { PruneSingleObjectiveOperators(encoding); } } } } private void PruneSingleObjectiveOperators(IEncoding encoding) { if (encoding != null && encoding.Operators.Any(x => x is ISingleObjectiveOperator && !(x is IMultiObjectiveOperator))) encoding.Operators = encoding.Operators.Where(x => !(x is ISingleObjectiveOperator) || x is IMultiObjectiveOperator).ToList(); foreach (var multiOp in Encoding.Operators.OfType()) { foreach (var soOp in multiOp.Operators.Where(x => x is ISingleObjectiveOperator).ToList()) { multiOp.RemoveOperator(soOp); } } } protected override void OnEvaluatorChanged() { base.OnEvaluatorChanged(); ParameterizeOperators(); } private void ParameterizeOperators() { foreach (var op in Operators.OfType()) op.EvaluateFunc = Evaluate; foreach (var op in Operators.OfType()) op.AnalyzeAction = Analyze; } #region IMultiObjectiveHeuristicOptimizationProblem Members IParameter IMultiObjectiveHeuristicOptimizationProblem.MaximizationParameter { get { return Parameters["Maximization"]; } } IMultiObjectiveEvaluator IMultiObjectiveHeuristicOptimizationProblem.Evaluator { get { return Evaluator; } } #endregion } }