version to 1.1 | added eliteVectorMetadata for fitness functions |

some more config possibilities  |  fitness function call data over stdIn

geneticOptimization.conf

@@ -21,7 +21,7 @@ showMonitorGui=true
 
 # Whether to use an in-memory or a persistent cache. In the case of runtimePerformanceCheck=true, the caches are disabled
 
-# Weak references for an in-memory cache. Can help with exploding memory consumption 
+# Weak references for an in-memory cache. Can help with exploding memory consumption
 weakRefCache=false
 # If set, a persistent cache is used. If the value is empty, GenIe uses an in-memory cache
 persistentCacheDir=
@@ -54,6 +54,9 @@ constantRandomSeed=true
 # proportionate, helps to avoid early dominance of one/two fit candidates. Amplifies minor fitness differences in more mature populations)
 selectionStrategy=SigmaScaling
 
+# The probability a vector value will mutate to another random value from its value range when a vector is created from its parents
+mutationProbability=0.01
+
 # The size of the first population. After the first population, the evolution goes further with the specified population size. This is to start wide
 # spreaded and to evolve the best candidates more focused afterwards. Can be relevant if you have huge fitness calculation runtimes where every
 # calculation counts.
@@ -123,10 +126,18 @@ firstGenerationParentMetadata=
    initialMetadataAtt_2=initVal_2
 }
 
+# The fitness function call for a candidate vector recieves the metadata of the top N candidate vectors from its parent population for information.
+# This can e.g. be used for population based training. Here you can specify the count for the top N list. This value has NO influence to the genetic
+# optimization, it is just about metadata for the fitness function
+eliteMetadataCount=3
+
 # For using GenIe standalone, you can specify an exec call for the fitness function. This executable will be called for each candidate vector
-# evaluation, receiving the candidate vector values as invocation arguments. The last invocation argument will be the parents metadata as Json string.
-# GenIe will wait for the process to terminate, looking for a calculation result in its output. This calculation result must follow this example Json
-# structure, and should be somewhere at the end of the processes output:
+# evaluation, receiving the candidate vector values as invocation arguments (just strings from the configured value range), or over stdIn if enabled.
+# The forelast invocation argument will be the parents metadata (json), the last argument the metadata from the top N candidate vectors of the parent
+# population, also as Json string, simply as returned from the parents fitness function invocation.
+# GenIe will wait for the process to terminate, looking for a calculation result in its output (stdOut). This calculation result have to be like
+# the following example Json structure, and should be somewhere at the end of the processes output. GenIe have a look the tail of the output only for
+# performance purposes. Configure the tail length with consideredTailLength.
 # {
 #   "fitness":0.0,
 #   "metadata":{
@@ -135,6 +146,24 @@ firstGenerationParentMetadata=
 #   }
 # }
 fitnessFunctionExecPath=sum23TestFitnessFunction.sh
+consideredTailLength=10000
+# It true, all input to the fitness function process will be given over standard input as json instead of process arguments. The format given to stdIn is as follows
+# {
+#   "candidateVectorParamNames":[“paramName1”, “paramName2”, “paramName3”,..],
+#   "candidateVector":[“value1”, “value2”, “value3”,..],
+#   "parentsMetadata":[
+#        {"someMetadataKeyParent1_1":"someMetadataValue", "someMetadataKeyParent1_N":"someMetadataValue"},
+#        {"someMetadataKeyParent2_1":"someMetadataValue", "someMetadataKeyParent2_N":"someMetadataValue"},
+#      ],
+#   "eliteMetadata":[
+#        {"someMetadataKeyElite1_1":"someMetadataValue", "someMetadataKeyElite1_N":"someMetadataValue"},
+#        {"someMetadataKeyElite2_1":"someMetadataValue", "someMetadataKeyElite2_N":"someMetadataValue"},
+#        ...
+#      ]
+# }
+inputOverStdIn=false
+# The tail of the fitness function output should be logged
+logExecOutputTail=true
 
 
 

pom.xml

@@ -3,7 +3,7 @@
     <modelVersion>4.0.0</modelVersion>
     <groupId>de.dfki.sds</groupId>
     <artifactId>genIe</artifactId>
-    <version>1.0</version>
+    <version>1.1</version>
     <packaging>jar</packaging>
     <name>genIe</name>
     <url>http://leechcrawler.github.com/leech/</url>

src/main/java/de/dfki/sds/genie/FeldWaldWiese.java

@@ -2,70 +2,39 @@ package de.dfki.sds.genie;
 
 
 
-import javax.print.*;
-import javax.print.attribute.HashPrintRequestAttributeSet;
-import javax.print.attribute.PrintRequestAttributeSet;
-import javax.print.attribute.standard.JobSheets;
-import javax.print.attribute.standard.MediaSizeName;
-import java.io.IOException;
+import de.dfki.inquisitor.streams.StreamUtilz;
+
+import java.io.BufferedWriter;
 import java.io.OutputStream;
+import java.io.OutputStreamWriter;
+import java.util.LinkedList;
 
 
 
 public class FeldWaldWiese
 {
 
-    public static void main(String[] args)
+    public static void main(String[] args) throws Exception
     {
-        DocFlavor[] docFlavorsAccepted =
-                {DocFlavor.INPUT_STREAM.JPEG, DocFlavor.INPUT_STREAM.JPEG, DocFlavor.INPUT_STREAM.PNG, DocFlavor.INPUT_STREAM.GIF, DocFlavor.INPUT_STREAM.TEXT_PLAIN_HOST,
-                 DocFlavor.INPUT_STREAM.TEXT_HTML_HOST, DocFlavor.INPUT_STREAM.TEXT_HTML_HOST, DocFlavor.INPUT_STREAM.POSTSCRIPT,//7
-                 DocFlavor.INPUT_STREAM.PDF};
-
-
-        int iFlavour = 7;
-        StreamPrintServiceFactory[] prservFactories =
-                StreamPrintServiceFactory.lookupStreamPrintServiceFactories(null, null);
-        if (null == prservFactories || 0 >= prservFactories.length)
-        {
-            System.err.println("sErrNoPrintService");
-            System.exit(2);
-        }
-
-
-        DocFlavor flavor = docFlavorsAccepted[iFlavour];
-        PrintRequestAttributeSet aset = new HashPrintRequestAttributeSet();
-        aset.add(MediaSizeName.ISO_A4);
-
-
-
-        StreamPrintService streamPrintService = prservFactories[0].getPrintService(new OutputStream()
-        {
-            @Override
-            public void write(int b) throws IOException
-            {
-
-            }
-        });
-        JobSheets jobSheets = (JobSheets) streamPrintService.getDefaultAttributeValue(JobSheets.class);
-        System.out.println(jobSheets);
+        LinkedList<String> lCommand = new LinkedList<>();
+        lCommand.add("/home/reuschling/muell/test.sh");
+        lCommand.add("bla");
 
+        ProcessBuilder builder = new ProcessBuilder(lCommand);
+        Process process = builder.start();
 
+        // TODO anstatt alles über Aufrufargumente mitzugeben, die Daten in stdIn schieben
+        OutputStreamWriter outputStreamWriter = new OutputStreamWriter(process.getOutputStream());
+        outputStreamWriter.write("'mein gepipter input mit\nleerzeichen'");
+        outputStreamWriter.close();
 
-        PrintService[] pservices = PrintServiceLookup.lookupPrintServices(flavor, aset);
-        if (pservices.length > 0)
-        {
-            PrintService service = pservices[0];
-            DocPrintJob pj = service.createPrintJob();
 
-            boolean mNoJobSheet = false;
-            JobSheets js = (JobSheets) (service.getDefaultAttributeValue(JobSheets.class));
-            if (js != null && js.equals(JobSheets.NONE))
-            {
-                mNoJobSheet = true;
-            }
+        int returnCode = process.waitFor();
+        if (returnCode != 0)
+            throw new Exception(String.format("Fitness function exec call returned not '0' but '%s'", returnCode));
 
-            System.out.println(mNoJobSheet);
-        }
+        int iTailLength = 10000;
+        String strOutputTail = StreamUtilz.getStreamTail(process.getInputStream(), iTailLength);
+        System.out.println(strOutputTail + "'");
     }
 }

src/main/java/de/dfki/sds/genie/GenIe.java

@@ -11,12 +11,14 @@ import de.dfki.inquisitor.streams.StreamUtilz;
 import de.dfki.sds.genie.genetic.CandidateVectorWithMetadata;
 import de.dfki.sds.genie.genetic.GeneticParamOptimizer;
 import de.dfki.sds.genie.genetic.GeneticRunConfig;
+import lombok.Data;
 import lombok.Setter;
 import lombok.experimental.Accessors;
 import org.slf4j.LoggerFactory;
 import picocli.CommandLine;
 
 import java.io.File;
+import java.io.OutputStreamWriter;
 import java.nio.file.Path;
 import java.util.HashMap;
 import java.util.LinkedHashMap;
@@ -34,16 +36,20 @@ public class GenIe extends GeneticParamOptimizer
 
 
 
-    public static void main(String[] args)
+    @Accessors(chain = true, prefix = {"m_d", "m_f", "m_str", "m_l", "m_hs", "m_s", "m_", "d", "f", "str", "l", "hs", "s", ""})
+    @Data
+    public static class FitnessFunctionStdInInput
     {
+        List<String> candidateVectorParamNames;
+        List<String> candidateVector;
+        List<HashMap<String, String>> parentsMetadata;
+        List<HashMap<String, String>> eliteMetadata;
+    }
 
-        // TODO Joachim Projekt
-
-        // TODO Andreas fragen wg openSource, vorstellen in der Gruppe
 
-        // TODO Christoph zeigen, weitere Schritte
 
-        // TODO In meiner neuen HomePage referenzieren
+    public static void main(String[] args)
+    {
 
         GenIe geneticOptimizer = new GenIe();
 
@@ -65,10 +71,6 @@ public class GenIe extends GeneticParamOptimizer
 
 
 
-
-
-
-
     @CommandLine.Option(names = {"-c", "--configFile"}, description = "The file path to the genIe configuration that should be used")
     protected String m_strConfigPath = "geneticOptimization.conf";
     @Setter
@@ -100,7 +102,7 @@ public class GenIe extends GeneticParamOptimizer
                 return m_fitnessCalculator.calculateFitness(candidate, population);
 
 
-            // we invoke the configred exec call as default
+            // we invoke the configured exec call as default
 
             LinkedList<String> lCommand = new LinkedList<>();
 
@@ -108,22 +110,42 @@ public class GenIe extends GeneticParamOptimizer
             strCommandPath = Path.of(strCommandPath).toAbsolutePath().toString();
 
             lCommand.add(strCommandPath);
-            lCommand.addAll(candidate.getCandidateVector());
-
-            String strJson = JsonWriter.objectToJson(candidate.getParentMetadata(), CollectionUtilz.createHashMap(JsonWriter.TYPE, false, JsonWriter.PRETTY_PRINT, true));
-            lCommand.add(strJson);
-
-
+            if (!m_geneticRunConfig.m_geneticConfig.getUniqueAsBoolean("inputOverStdIn"))
+            {
+                lCommand.addAll(candidate.getCandidateVector());
+                // parents
+                String strJson4ParentMetadata =
+                        JsonWriter.objectToJson(candidate.getParentMetadata(), CollectionUtilz.createHashMap(JsonWriter.TYPE, false, JsonWriter.PRETTY_PRINT, true));
+                lCommand.add(strJson4ParentMetadata);
+                // elite
+                String strJson4TopNMetadata =
+                        JsonWriter.objectToJson(candidate.getTopNParentGenerationMetadata(), CollectionUtilz.createHashMap(JsonWriter.TYPE, false, JsonWriter.PRETTY_PRINT, true));
+                lCommand.add(strJson4TopNMetadata);
+            }
 
             ProcessBuilder builder = new ProcessBuilder(lCommand);
             Process process = builder.start();
-
-            String strOutputTail = StreamUtilz.getStreamTail(process.getInputStream(), 10000);
+            if (m_geneticRunConfig.m_geneticConfig.getUniqueAsBoolean("inputOverStdIn"))
+            {
+                OutputStreamWriter outputStreamWriter = new OutputStreamWriter(process.getOutputStream());
+
+                FitnessFunctionStdInInput stdInInput =
+                        new FitnessFunctionStdInInput().setCandidateVectorParamNames(candidate.getCandidateVectorParamNames()).setCandidateVector(candidate.getCandidateVector())
+                                .setParentsMetadata(candidate.getParentMetadata()).setEliteMetadata(candidate.getTopNParentGenerationMetadata());
+                String strJson4StdInInput = JsonWriter.objectToJson(stdInInput, CollectionUtilz.createHashMap(JsonWriter.TYPE, false, JsonWriter.PRETTY_PRINT, true));
+                outputStreamWriter.write(strJson4StdInInput);
+                outputStreamWriter.close();
+            }
 
             int returnCode = process.waitFor();
             if (returnCode != 0)
                 throw new Exception(String.format("Fitness function exec call returned not '0' but '%s'", returnCode));
 
+            int iTailLength = m_geneticRunConfig.m_geneticConfig.getUniqueAsInteger("consideredTailLength");
+            String strOutputTail = StreamUtilz.getStreamTail(process.getInputStream(), iTailLength);
+
+            if (m_geneticRunConfig.m_geneticConfig.getUniqueAsBoolean("logExecOutputTail"))
+                LoggerFactory.getLogger(GenIe.class.getName()).info(String.format("Fitness exec call output tail (recognized length of %s):\n%s", iTailLength, strOutputTail));
 
 
             // das wollen wir parsen {"fitness":0,"metadata":{"shellVar":"hier","shellVector":"2,15,30,0"}}

src/main/java/de/dfki/sds/genie/genetic/CandidateVectorWithMetadata.java

@@ -18,40 +18,45 @@ import java.util.List;
 public class CandidateVectorWithMetadata
 {
 
-    public CandidateVectorWithMetadata(List<String> paramNames)
-    {
-
-    }
     /**
      * The vector that will be optimized
      */
     List<String> m_lCandidateVector;
-
     /**
      * The names of the vector parameters, in the same order as the values inside the candidate
      */
     List<String> m_lCandidateVectorParamNames;
-
     /**
+     * TODO - wollen wir hier wirklich eine HashMap oder schleifen wir die einfach unverarbeitet durch
      * Optional. Some arbitrary metadata given from the cost function (or maybe initially from the config for the first generation)
      */
     HashMap<String, String> m_hsMetadata = new HashMap<>();
-
     /**
      * Optional. The metadata of the parents, will be given to the cost function for information
      */
     List<HashMap<String, String>> m_lParentMetadata;
-
+    /**
+     * Optional. The metadata of the topN parent generation candidates, will be given to the cost function for information
+     */
+    List<HashMap<String, String>> m_lTopNParentGenerationMetadata;
     /**
      * Optional. The scores of the parents in the same order as the parents metadata. Will be given to the cost function for information
      */
     List<Double> m_lParentScores;
-
+    /**
+     * Optional. The scores of the topN parent generation candidates in the same order as the according metadata list. Will be given to the cost function for information
+     */
+    List<Double> m_lTopNParentGenerationScores;
     /**
      * Will be set by the system
      */
     Double m_dFitnessScore = Double.NaN;
 
+    public CandidateVectorWithMetadata(List<String> candidateVectorParamNames)
+    {
+        m_lCandidateVectorParamNames = candidateVectorParamNames;
+    }
+
 
 
     public CandidateVectorWithMetadata cloneDeep()
@@ -71,6 +76,15 @@ public class CandidateVectorWithMetadata
         }
         if (this.getParentScores() != null)
             clone.setParentScores(new ArrayList<>(this.getParentScores()));
+
+        if (this.getTopNParentGenerationMetadata() != null)
+        {
+            List<HashMap<String, String>> lTopNMetadataNew = this.getParentMetadata().stream().map(HashMap::new).toList();
+            clone.setTopNParentGenerationMetadata(lTopNMetadataNew);
+        }
+        if (this.getTopNParentGenerationScores() != null)
+            clone.setTopNParentGenerationScores(new ArrayList<>(this.getTopNParentGenerationScores()));
+
         clone.setFitnessScore(this.getFitnessScore());
 
         return clone;

src/main/java/de/dfki/sds/genie/genetic/GeneticParamOptimizer.java

@@ -81,6 +81,7 @@ public abstract class GeneticParamOptimizer
 
 
 
+
         @Override
         protected List<CandidateVectorWithMetadata> mate(CandidateVectorWithMetadata parent1, CandidateVectorWithMetadata parent2, int numberOfCrossoverPoints, Random rng)
         {
@@ -156,8 +157,6 @@ public abstract class GeneticParamOptimizer
                 {
                     lStartTime = System.currentTimeMillis();
 
-                    // TODO hier wäre noch geil, wenn man zu der runtime auch noch die fitness anzeigen könnte, um einen Eindruck zu kriegen, wie sehr die Qualität leidet. Das
-                    //  wäre jetzt möglich, da wir den ins CandidateVectorWithMetadata mit reinschreiben
                     double dCalculatedFitness = GeneticParamOptimizer.this.calculateFitness(candidate, population);
 
                     candidate.setFitnessScore(dCalculatedFitness);
@@ -748,7 +747,7 @@ public abstract class GeneticParamOptimizer
         List<EvolutionaryOperator<CandidateVectorWithMetadata>> lEvolutionaryOperators = new LinkedList<>();
 
         // hier werden einzelne Bestandteile eines Gewinners einer Population evtl. mutiert
-        lEvolutionaryOperators.add(new RecoParamMutation(new Probability(0.1)));
+        lEvolutionaryOperators.add(new RecoParamMutation(new Probability(m_geneticRunConfig.m_mutationProbability)));
         // hier (er)zeugen die Gewinner einer Population ihre Nachkommen
         lEvolutionaryOperators.add(new CandidateVectorWithMetadataCrossover());
         // die Götter haben das letzte Wort

src/main/java/de/dfki/sds/genie/genetic/GeneticRunConfig.java

@@ -39,9 +39,11 @@ public class GeneticRunConfig
     public boolean m_bVerbose = false;
     public boolean m_bWeakRefCache = false;
     public int m_eliteCount;
+    public int m_eliteMetadataCount4Metadata;
     public int m_epochLength;
     public int m_firstPopulationSize;
     public String m_selectionStrategy;
+    public float m_mutationProbability;
     public MultiValueConfiguration m_geneticConfig;
     public int m_iEvaluateLastWinnersCount;
     public int m_islandCount;
@@ -92,6 +94,7 @@ public class GeneticRunConfig
         }
 
         m_selectionStrategy = m_geneticConfig.getUniqueAsString("selectionStrategy");
+        m_mutationProbability = geneticConfig.getUniqueAsFloat("mutationProbability");
 
         m_firstPopulationSize = geneticConfig.getUniqueAsInteger("firstPopulationSize");
         m_populationSize = geneticConfig.getUniqueAsInteger("populationSize");
@@ -112,6 +115,7 @@ public class GeneticRunConfig
 
         m_geneticConfig.getUniqueAsConfiguration("firstGenerationParentMetadata").entryList()
                 .forEach(entry -> m_firstGenerationParentMetadata.put(entry.getKey(), entry.getValue().getAsString()));
+        m_eliteMetadataCount4Metadata = geneticConfig.getUniqueAsInteger("eliteMetadataCount");
 
 
         return this;

src/main/java/de/dfki/sds/genie/genetic/NonShrinkingGenerationalEvolutionEngine.java

@@ -28,16 +28,11 @@ import java.util.concurrent.CopyOnWriteArraySet;
 public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvolutionEngine<CandidateVectorWithMetadata>
 {
 
+    protected final GeneticParamOptimizer m_geneticParamOptimizer;
     protected CandidateFactory<CandidateVectorWithMetadata> candidateFactory;
-
     protected EvolutionaryOperator<CandidateVectorWithMetadata> evolutionScheme;
-
     protected FitnessEvaluator<? super CandidateVectorWithMetadata> fitnessEvaluator;
-
     protected CandidatesEntropyAnalyzer m_candidatesEntropyAnalyzer = new CandidatesEntropyAnalyzer();
-
-    protected final GeneticParamOptimizer m_geneticParamOptimizer;
-
     protected int m_populationSize;
 
     protected Set<EvolutionObserver<? super CandidateVectorWithMetadata>> observers = new CopyOnWriteArraySet<EvolutionObserver<? super CandidateVectorWithMetadata>>();
@@ -50,8 +45,10 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
 
 
 
-    public NonShrinkingGenerationalEvolutionEngine(CandidateFactory<CandidateVectorWithMetadata> candidateFactory, EvolutionaryOperator<CandidateVectorWithMetadata> evolutionScheme,
-                                                   FitnessEvaluator<? super CandidateVectorWithMetadata> fitnessEvaluator, SelectionStrategy<? super CandidateVectorWithMetadata> selectionStrategy, Random rng, int populationSize,
+    public NonShrinkingGenerationalEvolutionEngine(CandidateFactory<CandidateVectorWithMetadata> candidateFactory,
+                                                   EvolutionaryOperator<CandidateVectorWithMetadata> evolutionScheme,
+                                                   FitnessEvaluator<? super CandidateVectorWithMetadata> fitnessEvaluator,
+                                                   SelectionStrategy<? super CandidateVectorWithMetadata> selectionStrategy, Random rng, int populationSize,
                                                    GeneticParamOptimizer geneticParamOptimizer)
     {
         super(candidateFactory, evolutionScheme, fitnessEvaluator, selectionStrategy, rng);
@@ -70,8 +67,10 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
 
 
 
-    public NonShrinkingGenerationalEvolutionEngine(CandidateFactory<CandidateVectorWithMetadata> candidateFactory, EvolutionaryOperator<CandidateVectorWithMetadata> evolutionScheme,
-                                                   InteractiveSelection<CandidateVectorWithMetadata> selectionStrategy, Random rng, int populationSize, GeneticParamOptimizer geneticParamOptimizer)
+    public NonShrinkingGenerationalEvolutionEngine(CandidateFactory<CandidateVectorWithMetadata> candidateFactory,
+                                                   EvolutionaryOperator<CandidateVectorWithMetadata> evolutionScheme,
+                                                   InteractiveSelection<CandidateVectorWithMetadata> selectionStrategy, Random rng, int populationSize,
+                                                   GeneticParamOptimizer geneticParamOptimizer)
     {
         super(candidateFactory, evolutionScheme, selectionStrategy, rng);
 
@@ -108,13 +107,14 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
     /**
      * {@inheritDoc}
      */
-    public List<EvaluatedCandidate<CandidateVectorWithMetadata>> evolvePopulation(int populationSize, int eliteCount, Collection<CandidateVectorWithMetadata> seedCandidates, TerminationCondition... conditions)
+    public List<EvaluatedCandidate<CandidateVectorWithMetadata>> evolvePopulation(int populationSize, int eliteCount, Collection<CandidateVectorWithMetadata> seedCandidates,
+                                                                                  TerminationCondition... conditions)
     {
-        if(eliteCount < 0 || eliteCount >= populationSize)
+        if (eliteCount < 0 || eliteCount >= populationSize)
         {
             throw new IllegalArgumentException("Elite count must be non-negative and less than population size.");
         }
-        if(conditions.length == 0)
+        if (conditions.length == 0)
         {
             throw new IllegalArgumentException("At least one TerminationCondition must be specified.");
         }
@@ -131,7 +131,7 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
         boolean bFirst = true;
         while (satisfiedConditions == null)
         {
-            if(bFirst)
+            if (bFirst)
             {
                 evaluatedPopulation = evaluatePopulation(population);
                 bFirst = false;
@@ -140,7 +140,8 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
                 evaluatedPopulation = nextEvolutionStep(evaluatedPopulation, eliteCount, rng);
 
             EvolutionUtils.sortEvaluatedPopulation(evaluatedPopulation, fitnessEvaluator.isNatural());
-            PopulationData<CandidateVectorWithMetadata> data = EvolutionUtils.getPopulationData(evaluatedPopulation, fitnessEvaluator.isNatural(), eliteCount, currentGenerationIndex, startTime);
+            PopulationData<CandidateVectorWithMetadata> data =
+                    EvolutionUtils.getPopulationData(evaluatedPopulation, fitnessEvaluator.isNatural(), eliteCount, currentGenerationIndex, startTime);
             // Notify observers of the state of the population.
             notifyPopulationChange(data);
 
@@ -149,11 +150,11 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
 
 
 
-            if(m_geneticParamOptimizer.m_geneticRunConfig.m_bVerbose)
+            if (m_geneticParamOptimizer.m_geneticRunConfig.m_bVerbose)
             {
                 String strMetadata = String.format(
-                        "Analyzed set: Generation %s with population size %d, mean fitness: %.2f, fitness standard deviation %.2f, needed %s\nThe best vector in the set is "
-                                + "highlighted",
+                        "Analyzed set: Generation %s with population size %d, mean fitness: %.2f, fitness standard deviation %.2f, needed %s\nThe best vector in the set is " +
+                                "highlighted",
                         data.getGenerationNumber(), data.getPopulationSize(), data.getMeanFitness(), data.getFitnessStandardDeviation(),
                         StopWatch.formatTimeDistance(data.getElapsedTime()));
 
@@ -200,7 +201,7 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
 
     public List<TerminationCondition> getSatisfiedTerminationConditions()
     {
-        if(satisfiedTerminationConditions == null)
+        if (satisfiedTerminationConditions == null)
         {
             throw new IllegalStateException("EvolutionEngine has not terminated.");
         }
@@ -216,25 +217,55 @@ public class NonShrinkingGenerationalEvolutionEngine extends GenerationalEvoluti
      * {@inheritDoc}
      */
     @Override
-    protected List<EvaluatedCandidate<CandidateVectorWithMetadata>> nextEvolutionStep(List<EvaluatedCandidate<CandidateVectorWithMetadata>> evaluatedPopulation, int eliteCount, Random rng)
+    protected List<EvaluatedCandidate<CandidateVectorWithMetadata>> nextEvolutionStep(List<EvaluatedCandidate<CandidateVectorWithMetadata>> evaluatedPopulation, int eliteCount,
+                                                                                      Random rng)
     {
-        List<CandidateVectorWithMetadata> population = new ArrayList<CandidateVectorWithMetadata>(evaluatedPopulation.size());
-
         // First perform any elitist selection.
-        List<CandidateVectorWithMetadata> elite = new ArrayList<CandidateVectorWithMetadata>(eliteCount);
-        Iterator<EvaluatedCandidate<CandidateVectorWithMetadata>> iterator = evaluatedPopulation.iterator();
-        while (elite.size() < eliteCount)
+        List<CandidateVectorWithMetadata> lElite = new ArrayList<>(eliteCount);
+        int iEliteMetadataCount4Metadata = m_geneticParamOptimizer.m_geneticRunConfig.m_eliteMetadataCount4Metadata;
+        List<HashMap<String, String>> lEliteMetadata4Metadata = new ArrayList<>(iEliteMetadataCount4Metadata);
+        List<Double> lEliteScores4Metadata = new ArrayList<>(iEliteMetadataCount4Metadata);
+
+        //  wir ermitteln die topN candidaten - einmal für die Elite die weiterleben darf, einmal für unsere KandidatenMetadaten
+        //  evaluatedPopulation is sorted, fitnessEvaluator.isNatural() is considered
+        for (EvaluatedCandidate<CandidateVectorWithMetadata> elitistEvaluated : evaluatedPopulation)
         {
-            elite.add(iterator.next().getCandidate());
+            CandidateVectorWithMetadata elitist = elitistEvaluated.getCandidate();
+
+            if (lElite.size() < eliteCount)
+                lElite.add(elitist);
+            if (lEliteMetadata4Metadata.size() < iEliteMetadataCount4Metadata)
+            {
+                lEliteMetadata4Metadata.add(new HashMap<>(elitist.getMetadata()));
+                lEliteScores4Metadata.add(elitistEvaluated.getFitness());
+            }
+
+            if (lElite.size() == eliteCount && lEliteMetadata4Metadata.size() == iEliteMetadataCount4Metadata)
+                break;
         }
+
+
+
         // Then select candidates that will be operated on to create the evolved portion of the next generation.
         // We use the configured generation size instead of the current one to ensure fixed generation sizes
-        population.addAll(selectionStrategy.select(evaluatedPopulation, fitnessEvaluator.isNatural(), m_populationSize - eliteCount, rng));
-        // Then evolve the population.
-        population = evolutionScheme.apply(population, rng);
+        List<CandidateVectorWithMetadata> lSelectedParents =
+                new ArrayList<>(selectionStrategy.select(evaluatedPopulation, fitnessEvaluator.isNatural(), m_populationSize - eliteCount, rng));
+
+        // Then evolve the population (generate new candidate vectors out of the selected parents).
+        List<CandidateVectorWithMetadata> lChildren = evolutionScheme.apply(lSelectedParents, rng);
+
         // When the evolution is finished, add the elite to the population.
-        population.addAll(elite);
-        return evaluatePopulation(population);
+        lChildren.addAll(lElite);
+        // topN Elite as information to the cost function
+        lChildren.forEach(child -> child.setTopNParentGenerationMetadata(lEliteMetadata4Metadata));
+        lChildren.forEach(child -> child.setTopNParentGenerationScores(lEliteScores4Metadata));
+
+
+
+        // finally, the fitness function will be performed on each generated candidate in this new population
+        List<EvaluatedCandidate<CandidateVectorWithMetadata>> lEvaluatedChildren = evaluatePopulation(lChildren);
+
+        return lEvaluatedChildren;
     }
 
 

src/test/java/de/dfki/sds/genie/Sum23Test.java

@@ -18,16 +18,16 @@ public class Sum23Test
     public static void main(String[] args)
     {
 
-        CandidateVectorWithMetadata bestVector = new GenIe().setFitnessCalculator((candidate, population) -> {
-            int iSum = candidate.getCandidateVector().stream().mapToInt(Integer::valueOf).sum();
-
-            if (iSum == 23)
-                return 0.9d;
-
-            return 0d;
-        }).optimizeParams("src/test/java/de/dfki/sds/genie/geneticOptimization.conf");