Title: The JavaEvA
- *Description:
- *Copyright: Copyright (c) 2003
- *Company:
+/** + * A memetic algorithm by hannes planatscher. The local search strategy can only + * be applied to problems which implement the InterfaceLocalSearchable else the + * local search will not be activated at all. + *+ * Title: The JavaEvA + *
+ *+ * Description: + *
+ *+ * Copyright: Copyright (c) 2003 + *
+ *+ * Company: + *
+ * * @author not attributable * @version 1.0 */ -public class MemeticAlgorithm implements InterfaceOptimizer, java.io.Serializable { +public class MemeticAlgorithm implements InterfaceOptimizer, + java.io.Serializable { - private int localSearchSteps = 1; - private int subsetsize = 5; - private int globalSearchSteps = 1; - private boolean lamarckism = true; - // int counter = 0; !? - // int maxfunctioncalls = 1000; !? + /** + * serial version uid. + */ + private static final long serialVersionUID = -1730086430763348568L; - private boolean TRACE = false; - private String m_Identifier = ""; - private InterfaceOptimizationProblem m_Problem = new F1Problem(); - private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm(); - private InterfaceSelection selectorPlug = new SelectBestIndividuals(); - transient private InterfacePopulationChangedEventListener m_Listener; + private int localSearchSteps = 1; + private int subsetsize = 5; - public MemeticAlgorithm() { + private int globalSearchSteps = 1; - } + private boolean lamarckism = true; - public MemeticAlgorithm(MemeticAlgorithm a) { - // this.m_Population = (Population)a.m_Population.clone(); - this.m_Problem = (InterfaceLocalSearchable)a.m_Problem.clone(); - this.m_GlobalOptimizer = (InterfaceOptimizer)a.m_GlobalOptimizer; - this.selectorPlug = (InterfaceSelection)a.selectorPlug; - this.m_Identifier = a.m_Identifier; - this.localSearchSteps = a.localSearchSteps; - this.subsetsize = a.subsetsize; - this.globalSearchSteps = a.globalSearchSteps; - this.lamarckism = a.lamarckism; - } + // int counter = 0; !? + // int maxfunctioncalls = 1000; !? - public Object clone() { - return (Object) new MemeticAlgorithm(this); - } + private boolean TRACE = false; - public void initByPopulation(Population pop, boolean reset) { - this.setPopulation((Population) pop.clone()); - if (reset)this.getPopulation().init(); - this.m_Problem.evaluate(this.getPopulation()); - this.firePropertyChangedEvent("NextGenerationPerformed"); - } + private String m_Identifier = ""; - public void init() { - //counter = 0; - this.m_GlobalOptimizer.SetProblem(this.m_Problem); - this.m_GlobalOptimizer.init(); - this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation()); - this.firePropertyChangedEvent("NextGenerationPerformed"); - } + private InterfaceOptimizationProblem m_Problem = new F1Problem(); - /** This method will evaluate the current population using the - * given problem. - * @param population The population that is to be evaluated - */ - private void evaluatePopulation(Population population) { - this.m_Problem.evaluate(population); - population.incrGeneration(); - } + private InterfaceOptimizer m_GlobalOptimizer = new GeneticAlgorithm(); - public void optimize() { + private InterfaceSelection selectorPlug = new SelectBestIndividuals(); - if (TRACE) System.out.println("global search"); - this.m_GlobalOptimizer.optimize(); + transient private InterfacePopulationChangedEventListener m_Listener; - if ((this.m_GlobalOptimizer.getPopulation().getGeneration()%this.globalSearchSteps == 0) - && (this.localSearchSteps != 0) - && (this.m_Problem instanceof InterfaceLocalSearchable)){ - // here the local search is performed - if (TRACE) System.out.println("Performing local search on " +subsetsize+ " individuals."); - Population gop = this.m_GlobalOptimizer.getPopulation(); - Population subset = selectorPlug.selectFrom(gop, subsetsize); - Population subsetclone = new Population(); - for (int i = 0; i < subset.size(); i++) { - subsetclone.add(((AbstractEAIndividual)subset.get(i)).clone()); - } - if (subset.size() != subsetsize) System.out.println("ALERT! identic individual instances in subset"); - Hashtable antilamarckismcache = new Hashtable(); - if (!this.lamarckism) { - for (int i = 0; i < subset.size(); i++) { - AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i); - AbstractEAIndividual indyclone = (AbstractEAIndividual) subsetclone.get(i); - antilamarckismcache.put(indy, indyclone); - } - } + public MemeticAlgorithm() { - //int dosearchsteps = this.localSearchSteps; - double cost = ((InterfaceLocalSearchable)this.m_Problem).getLocalSearchStepFunctionCallEquivalent(); - //int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps * cost * subset.size()); - // nett aber total unn�tig-falsch man kann nicht davon ausgehen, dass man einen Fitnesscall Terminator hat.. -// if (calls > this.maxfunctioncalls) { -// int remainingfunctioncalls = this.maxfunctioncalls - gop.getFunctionCalls(); -// dosearchsteps = (int)Math.floor(((double) remainingfunctioncalls) / (cost * subsetsize)); -// stopit = true; -// } - for (int i = 0; i < localSearchSteps ; i++) { - ((InterfaceLocalSearchable)this.m_Problem).doLocalSearch(subsetclone); - } - this.m_Problem.evaluate(subsetclone); - if (this.lamarckism) { - gop.removeAll(subset); - gop.addPopulation(subsetclone); - } else { - for (int i = 0; i < subset.size(); i++) { - AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i); - try { - AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache.get(indy); - indy.SetFitness(newindy.getFitness()); - } - catch (Exception ex) { - System.out.println("indy not found in antilamarckismcache"); - } - } - } - // eigentlich muss hier noch subsetsize drauf, aber lassen wir das - gop.SetFunctionCalls(gop.getFunctionCalls() + (int) Math.round(localSearchSteps * cost * subset.size())); + } - if (TRACE) System.out.println("Population size after local search:" + gop.size()); + public MemeticAlgorithm(MemeticAlgorithm a) { + // this.m_Population = (Population)a.m_Population.clone(); + this.m_Problem = (InterfaceLocalSearchable) a.m_Problem.clone(); + this.m_GlobalOptimizer = (InterfaceOptimizer) a.m_GlobalOptimizer; + this.selectorPlug = (InterfaceSelection) a.selectorPlug; + this.m_Identifier = a.m_Identifier; + this.localSearchSteps = a.localSearchSteps; + this.subsetsize = a.subsetsize; + this.globalSearchSteps = a.globalSearchSteps; + this.lamarckism = a.lamarckism; + } - this.setPopulation(gop); - } + @Override + public Object clone() { + return new MemeticAlgorithm(this); + } - if (TRACE) System.out.println("function calls" + this.m_GlobalOptimizer.getPopulation().getFunctionCalls()); - this.firePropertyChangedEvent("NextGenerationPerformed"); - } + public void initByPopulation(Population pop, boolean reset) { + this.setPopulation((Population) pop.clone()); + if (reset) this.getPopulation().init(); + this.m_Problem.evaluate(this.getPopulation()); + this.firePropertyChangedEvent("NextGenerationPerformed"); + } - /** This method allows you to add the LectureGUI as listener to the Optimizer - * @param ea - */ - public void addPopulationChangedEventListener(InterfacePopulationChangedEventListener ea) { - this.m_Listener = ea; - } - /** Something has changed - */ - protected void firePropertyChangedEvent (String name) { - if (this.m_Listener != null) { - if (TRACE) System.out.println("firePropertyChangedEvent MA"); - this.m_Listener.registerPopulationStateChanged(this, name); - } - } + public void init() { + // counter = 0; + this.m_GlobalOptimizer.SetProblem(this.m_Problem); + this.m_GlobalOptimizer.init(); + this.evaluatePopulation(this.m_GlobalOptimizer.getPopulation()); + this.firePropertyChangedEvent("NextGenerationPerformed"); + } - /** This method will set the problem that is to be optimized - * @param problem - */ - public void SetProblem (InterfaceOptimizationProblem problem) { - this.m_Problem = problem; - this.m_GlobalOptimizer.SetProblem(this.m_Problem); - } - public InterfaceOptimizationProblem getProblem () { - return this.m_Problem; - } + /** + * This method will evaluate the current population using the given problem. + * + * @param population + * The population that is to be evaluated + */ + private void evaluatePopulation(Population population) { + this.m_Problem.evaluate(population); + population.incrGeneration(); + } - /** This method will return a string describing all properties of the optimizer - * and the applied methods. - * @return A descriptive string - */ - public String getStringRepresentation() { - String result = ""; - result += "Memetic Algorithm:\n"; - result += "Optimization Problem: "; - result += this.m_Problem.getStringRepresentationForProblem(this) +"\n"; - result += this.m_GlobalOptimizer.getStringRepresentation(); - return result; - } - /** This method allows you to set an identifier for the algorithm - * @param name The indenifier - */ - public void SetIdentifier(String name) { - this.m_Identifier = name; - } - public String getIdentifier() { - return this.m_Identifier; - } + public void optimize() { - /** This method is required to free the memory on a RMIServer, - * but there is nothing to implement. - */ - public void freeWilly() { + if (TRACE) System.out.println("global search"); + this.m_GlobalOptimizer.optimize(); - } -/********************************************************************************************************************** -* These are for GUI -*/ - /** This method returns a global info string - * @return description - */ - public String globalInfo() { - return "This is a basic generational Memetic Algorithm."; - } - /** This method will return a naming String - * @return The name of the algorithm - */ - public String getName() { - return "Memetic-Algorithm"; - } + if ((this.m_GlobalOptimizer.getPopulation().getGeneration() + % this.globalSearchSteps == 0) + && (this.localSearchSteps != 0) + && (this.m_Problem instanceof InterfaceLocalSearchable)) { + // here the local search is performed + if (TRACE) + System.out.println("Performing local search on " + subsetsize + + " individuals."); + Population gop = this.m_GlobalOptimizer.getPopulation(); + Population subset = selectorPlug.selectFrom(gop, subsetsize); + Population subsetclone = new Population(); + for (int i = 0; i < subset.size(); i++) { + subsetclone.add(((AbstractEAIndividual) subset.get(i)).clone()); + } + if (subset.size() != subsetsize) + System.err.println("ALERT! identical individual instances in subset"); + Hashtable antilamarckismcache = new Hashtable(); + if (!this.lamarckism) { + for (int i = 0; i < subset.size(); i++) { + AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i); + AbstractEAIndividual indyclone = (AbstractEAIndividual) subsetclone + .get(i); + antilamarckismcache.put(indy, indyclone); + } + } - /** Assuming that all optimizer will store thier data in a population - * we will allow acess to this population to query to current state - * of the optimizer. - * @return The population of current solutions to a given problem. - */ - public Population getPopulation() { - return this.m_GlobalOptimizer.getPopulation(); - } - public void setPopulation(Population pop){ - this.m_GlobalOptimizer.setPopulation(pop); - } - public String populationTipText() { - return "Edit the properties of the population used."; - } + // int dosearchsteps = this.localSearchSteps; + double cost = ((InterfaceLocalSearchable) this.m_Problem) + .getLocalSearchStepFunctionCallEquivalent(); + // int calls = gop.getFunctionCalls() + (int) Math.round(localSearchSteps + // * cost * subset.size()); + // nett aber total unn�tig-falsch man kann nicht davon ausgehen, dass man + // einen Fitnesscall Terminator hat.. + // if (calls > this.maxfunctioncalls) { + // int remainingfunctioncalls = this.maxfunctioncalls - + // gop.getFunctionCalls(); + // dosearchsteps = (int)Math.floor(((double) remainingfunctioncalls) / + // (cost * subsetsize)); + // stopit = true; + // } + for (int i = 0; i < localSearchSteps; i++) { + ((InterfaceLocalSearchable) this.m_Problem).doLocalSearch(subsetclone); + } + this.m_Problem.evaluate(subsetclone); + if (this.lamarckism) { + gop.removeAll(subset); + gop.addPopulation(subsetclone); + } else { + for (int i = 0; i < subset.size(); i++) { + AbstractEAIndividual indy = (AbstractEAIndividual) subset.get(i); + try { + AbstractEAIndividual newindy = (AbstractEAIndividual) antilamarckismcache + .get(indy); + indy.SetFitness(newindy.getFitness()); + } catch (Exception ex) { + System.err.println("individual not found in antilamarckismcache"); + } + } + } + // eigentlich muss hier noch subsetsize drauf, aber lassen wir das + gop.SetFunctionCalls(gop.getFunctionCalls() + + (int) Math.round(localSearchSteps * cost * subset.size())); + + if (TRACE) + System.out.println("Population size after local search:" + gop.size()); + + this.setPopulation(gop); + } + + if (TRACE) + System.out.println("function calls" + + this.m_GlobalOptimizer.getPopulation().getFunctionCalls()); + this.firePropertyChangedEvent("NextGenerationPerformed"); + } + + /** + * This method allows you to add the LectureGUI as listener to the Optimizer + * + * @param ea + */ + public void addPopulationChangedEventListener( + InterfacePopulationChangedEventListener ea) { + this.m_Listener = ea; + } + + /** + * Something has changed + */ + protected void firePropertyChangedEvent(String name) { + if (this.m_Listener != null) { + if (TRACE) System.out.println("firePropertyChangedEvent MA"); + this.m_Listener.registerPopulationStateChanged(this, name); + } + } + + /** + * This method will set the problem that is to be optimized + * + * @param problem + */ + public void SetProblem(InterfaceOptimizationProblem problem) { + this.m_Problem = problem; + this.m_GlobalOptimizer.SetProblem(this.m_Problem); + } + + public InterfaceOptimizationProblem getProblem() { + return this.m_Problem; + } + + /** + * This method will return a string describing all properties of the optimizer + * and the applied methods. + * + * @return A descriptive string + */ + public String getStringRepresentation() { + String result = ""; + result += "Memetic Algorithm:\n"; + result += "Optimization Problem: "; + result += this.m_Problem.getStringRepresentationForProblem(this) + "\n"; + result += this.m_GlobalOptimizer.getStringRepresentation(); + return result; + } + + /** + * This method allows you to set an identifier for the algorithm + * + * @param name + * The indenifier + */ + public void SetIdentifier(String name) { + this.m_Identifier = name; + } + + public String getIdentifier() { + return this.m_Identifier; + } + + /** + * This method is required to free the memory on a RMIServer, but there is + * nothing to implement. + */ + public void freeWilly() { + + } + + /* + * ======================================================================================== + * These are for GUI + */ + /** + * This method returns a global info string + * + * @return description + */ + public String globalInfo() { + return "This is a basic generational Memetic Algorithm."; + } + + /** + * This method will return a naming String + * + * @return The name of the algorithm + */ + public String getName() { + return "Memetic-Algorithm"; + } + + /** + * Assuming that all optimizer will store thier data in a population we will + * allow acess to this population to query to current state of the optimizer. + * + * @return The population of current solutions to a given problem. + */ + public Population getPopulation() { + return this.m_GlobalOptimizer.getPopulation(); + } + + public void setPopulation(Population pop) { + this.m_GlobalOptimizer.setPopulation(pop); + } + + public String populationTipText() { + return "Edit the properties of the population used."; + } + + /** + * Choose the global optimization strategy to use + * + * @param m_GlobalOptimizer + */ + public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) { + this.m_GlobalOptimizer = m_GlobalOptimizer; + this.m_GlobalOptimizer.SetProblem(this.getProblem()); + this.init(); + } + + public InterfaceOptimizer getGlobalOptimizer() { + return m_GlobalOptimizer; + } + + public String globalOptimizerTipText() { + return "Choose the global optimization strategy to use."; + } + + /** + * Choose the number of local search steps to perform per selected individual + * + * @param localSearchSteps + */ + public void setLocalSearchSteps(int localSearchSteps) { + this.localSearchSteps = localSearchSteps; + } + + public int getLocalSearchSteps() { + return localSearchSteps; + } + + public String localSearchStepsTipText() { + return "Choose the number of local search steps to perform per selected individual."; + } + + /** + * Choose the interval between the application of the local search + * + * @param globalSearchSteps + */ + public void setGlobalSearchSteps(int globalSearchSteps) { + this.globalSearchSteps = globalSearchSteps; + } + + public int getGlobalSearchSteps() { + return globalSearchSteps; + } + + public String globalSearchStepsTipText() { + return "Choose the interval between the application of the local search."; + } + + /** + * Choose the number of individual to be locally optimized + * + * @param subsetsize + */ + public void setSubsetsize(int subsetsize) { + this.subsetsize = subsetsize; + } public Population getAllSolutions() { return getPopulation(); } - /** Choose the global optimization strategy to use - * @param m_GlobalOptimizer - */ - public void setGlobalOptimizer(InterfaceOptimizer m_GlobalOptimizer) { - this.m_GlobalOptimizer = m_GlobalOptimizer; - this.m_GlobalOptimizer.SetProblem(this.getProblem()); - this.init(); - } - public InterfaceOptimizer getGlobalOptimizer() { - return m_GlobalOptimizer; - } - public String globalOptimizerTipText() { - return "Choose the global optimization strategy to use."; - } + public int getSubsetsize() { + return subsetsize; + } - /** Choose the number of local search steps to perform per selected individual - * @param localSearchSteps - */ - public void setLocalSearchSteps(int localSearchSteps) { - this.localSearchSteps = localSearchSteps; - } - public int getLocalSearchSteps() { - return localSearchSteps; - } - public String localSearchStepsTipText() { - return "Choose the number of local search steps to perform per selected individual."; - } + public String subsetsizeTipText() { + return "Choose the number of individual to be locally optimized."; + } - /** Choose the interval between the application of the local search - * @param globalSearchSteps - */ - public void setGlobalSearchSteps(int globalSearchSteps) { - this.globalSearchSteps = globalSearchSteps; - } - public int getGlobalSearchSteps() { - return globalSearchSteps; - } - public String globalSearchStepsTipText() { - return "Choose the interval between the application of the local search."; - } + /** + * Toggel between Lamarcksim and the Baldwin Effect + * + * @param lamarckism + */ + public void setLamarckism(boolean lamarckism) { + this.lamarckism = lamarckism; + } - /** Choose the number of individual to be locally optimized - * @param subsetsize - */ - public void setSubsetsize(int subsetsize) { - this.subsetsize = subsetsize; - } - public int getSubsetsize() { - return subsetsize; - } - public String subsetsizeTipText() { - return "Choose the number of individual to be locally optimized."; - } + public boolean getLamarckism() { + return this.lamarckism; + } - /** Toggel between Lamarcksim and the Baldwin Effect - * @param lamarckism - */ - public void setLamarckism(boolean lamarckism) { - this.lamarckism = lamarckism; - } - public boolean getLamarckism() { - return this.lamarckism; - } - public String lamarckismTipText() { - return "Toggel between Lamarcksim and the Baldwin Effect."; - } - public boolean isLamarckism() { - return lamarckism; - } -} \ No newline at end of file + public String lamarckismTipText() { + return "Toggel between Lamarcksim and the Baldwin Effect."; + } + + public boolean isLamarckism() { + return lamarckism; + } +}