#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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 . * * Author: Sabine Winkler */ #endregion using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.IntegerVectorEncoding; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.GrammaticalEvolution { /// /// Position Independent (PI) Grammatical Evolution Mapper /// ----------------------------------------------------------------------------------- /// Standard GE mappers: /// Rule = Codon Value % Number Of Rules /// /// 𝜋GE: /// 𝜋GE codons consist of (nont, rule) tuples, where nont may be one value from an "order" /// integer vector and rule may be one value from a "content" integer vector. /// /// Order: NT = nont % Num. NT ... determines, which non-terminal to expand next /// Content: Rule = rule % Num. Rules ... rule determination as with standard GE mappers /// /// Four mutation and crossover strategies possible: /// * Order-only: only "order" vector is modified, "content" vector is fixed (1:0), /// worst result according to [2] /// * Content-only: only "content" vector is modified, "order" vector is fixed (0:1), /// best result according to [2] /// * 𝜋GE: genetic operators are applied equally (1:1), /// * Content:Order: genetic operators are applied unequally (e.g. 2:1 or 1:2), /// /// Here, the "content-only" strategy is implemented, as it is the best solution according to [2] /// and it does not require much to change in the problem and evaluator classes. /// /// /// /// Described in /// /// [1] Michael O’Neill et al. 𝜋Grammatical Evolution. In: GECCO 2004. /// Vol. 3103. LNCS. Heidelberg: Springer-Verlag Berlin, 2004, pp. 617–629. /// url: http://dynamics.org/Altenberg/UH_ICS/EC_REFS/GP_REFS/GECCO/2004/31030617.pdf. /// /// [2] David Fagan et al. Investigating Mapping Order in πGE. IEEE, 2010 /// url: http://ncra.ucd.ie/papers/pigeWCCI2010.pdf /// [Item("PIGEMapper", "Position Independent (PI) Grammatical Evolution Mapper")] [StorableType("AED62020-ADCE-481A-9D00-F1D11648E79E")] public class PIGEMapper : GenotypeToPhenotypeMapper { private object nontVectorLocker = new object(); private IntegerVector nontVector; public IntegerVector NontVector { get { return nontVector; } set { nontVector = value; } } private static IntegerVector GetNontVector(IRandom random, IntMatrix bounds, int length) { IntegerVector v = new IntegerVector(length); v.Randomize(random, bounds); return v; } [StorableConstructor] protected PIGEMapper(bool deserializing) : base(deserializing) { } protected PIGEMapper(PIGEMapper original, Cloner cloner) : base(original, cloner) { } public PIGEMapper() : base() { } public override IDeepCloneable Clone(Cloner cloner) { return new PIGEMapper(this, cloner); } /// /// Maps a genotype (an integer vector) to a phenotype (a symbolic expression tree). /// PIGE approach. /// /// random number generator /// integer number range for genomes (codons) of the nont vector /// length of the nont vector to create /// grammar definition /// integer vector, which should be mapped to a tree /// phenotype (a symbolic expression tree) public override SymbolicExpressionTree Map(IRandom random, IntMatrix bounds, int length, ISymbolicExpressionGrammar grammar, IntegerVector genotype) { SymbolicExpressionTree tree = new SymbolicExpressionTree(); var rootNode = (SymbolicExpressionTreeTopLevelNode)grammar.ProgramRootSymbol.CreateTreeNode(); var startNode = (SymbolicExpressionTreeTopLevelNode)grammar.StartSymbol.CreateTreeNode(); rootNode.AddSubtree(startNode); tree.Root = rootNode; // Map can be called simultaniously on multiple threads lock (nontVectorLocker) { if (NontVector == null) { NontVector = GetNontVector(random, bounds, length); } } MapPIGEIteratively(startNode, genotype, grammar, genotype.Length, random); return tree; } /// /// Genotype-to-Phenotype mapper (iterative 𝜋GE approach, using a list of not expanded nonTerminals). /// /// first node of the tree with arity 1 /// integer vector, which should be mapped to a tree /// grammar to determine the allowed child symbols for each node /// maximum allowed subtrees (= number of used genomes) /// random number generator private void MapPIGEIteratively(ISymbolicExpressionTreeNode startNode, IntegerVector genotype, ISymbolicExpressionGrammar grammar, int maxSubtreeCount, IRandom random) { List nonTerminals = new List(); int genotypeIndex = 0; nonTerminals.Add(startNode); while (nonTerminals.Count > 0) { if (genotypeIndex >= maxSubtreeCount) { // if all genomes were used, only add terminal nodes to the remaining subtrees ISymbolicExpressionTreeNode current = nonTerminals[0]; nonTerminals.RemoveAt(0); current.AddSubtree(GetRandomTerminalNode(current, grammar, random)); } else { // Order: NT = nont % Num. NT int nt = NontVector[genotypeIndex] % nonTerminals.Count; ISymbolicExpressionTreeNode current = nonTerminals[nt]; nonTerminals.RemoveAt(nt); // Content: Rule = rule % Num. Rules ISymbolicExpressionTreeNode newNode = GetNewChildNode(current, genotype, grammar, genotypeIndex, random); int arity = SampleArity(random, newNode, grammar); current.AddSubtree(newNode); genotypeIndex++; // new node has subtrees, so add "arity" number of copies of this node to the nonTerminals list for (int i = 0; i < arity; ++i) { nonTerminals.Add(newNode); } } } } } }