diff --git a/Classification/FeatureSelection/pom.xml b/Classification/FeatureSelection/pom.xml
index bc1e72b94..c59237139 100644
--- a/Classification/FeatureSelection/pom.xml
+++ b/Classification/FeatureSelection/pom.xml
@@ -32,6 +32,11 @@
     </properties>
 
     <dependencies>
+        <dependency>
+            <groupId>org.ojalgo</groupId>
+            <artifactId>ojalgo</artifactId>
+            <version>53.0.0</version>
+        </dependency>
         <dependency>
             <groupId>${project.groupId}</groupId>
             <artifactId>tribuo-core</artifactId>
diff --git a/Classification/FeatureSelection/src/main/java/org/tribuo/classification/fs/FS_Wrapper_Approaches/Discreeting/TransferFunction.java b/Classification/FeatureSelection/src/main/java/org/tribuo/classification/fs/FS_Wrapper_Approaches/Discreeting/TransferFunction.java
new file mode 100644
index 000000000..de75c2dba
--- /dev/null
+++ b/Classification/FeatureSelection/src/main/java/org/tribuo/classification/fs/FS_Wrapper_Approaches/Discreeting/TransferFunction.java
@@ -0,0 +1,32 @@
+package FS_Wrapper_Approaches.Discreeting;
+
+import org.ojalgo.function.special.ErrorFunction;
+
+import java.util.function.DoubleUnaryOperator;
+
+/**
+ * Enumeration that contains the types of transfer functions in which they are used to define the type of transfer function
+ */
+public enum TransferFunction implements DoubleUnaryOperator {
+        V1, V2, V3, V4, S1, S2, S3, S4;
+
+        /**
+         * Applies this operator to the given value.
+         *
+         * @param value the operand as continuous value to be converted to either 1 or 0
+         * @return the operator result that is a d
+         */
+        @Override
+        public double applyAsDouble(double value) {
+                return switch (this) {
+                        case V1 -> Math.abs(ErrorFunction.erf(Math.sqrt(Math.PI) / 2 * value)) >= 0.5 ? 1 : 0;
+                        case V2 -> Math.abs(Math.tan(value)) >= 0.5 ? 1 : 0;
+                        case V3 -> Math.abs(value / Math.abs(1 + Math.pow(value, 2))) >= 0.5 ? 1 : 0;
+                        case V4 -> Math.abs(2 / Math.PI * Math.atan(Math.PI / 2 * value)) >= 0.5 ? 1 : 0;
+                        case S1 -> 1 / (1 + Math.pow(Math.E, - 2 * value)) >= 0.5 ? 1 : 0;
+                        case S2 -> 1 / (1 + Math.pow(Math.E, - value)) >= 0.5 ? 1 : 0;
+                        case S3 -> 1 / (1 + Math.pow(Math.E, - value / 2)) >= 0.5 ? 1 : 0;
+                        case S4 -> 1 / (1 + Math.pow(Math.E, - value / 3)) >= 0.5 ? 1 : 0;
+                };
+        }
+}
diff --git a/Classification/FeatureSelection/src/main/java/org/tribuo/classification/fs/FS_Wrapper_Approaches/Optimizers/CuckooSearchOptimizer.java b/Classification/FeatureSelection/src/main/java/org/tribuo/classification/fs/FS_Wrapper_Approaches/Optimizers/CuckooSearchOptimizer.java
new file mode 100644
index 000000000..2c6a80492
--- /dev/null
+++ b/Classification/FeatureSelection/src/main/java/org/tribuo/classification/fs/FS_Wrapper_Approaches/Optimizers/CuckooSearchOptimizer.java
@@ -0,0 +1,350 @@
+package FS_Wrapper_Approaches.Optimizers;
+
+import FS.Discreeting.TransferFunction;
+import com.oracle.labs.mlrg.olcut.util.Pair;
+import org.tribuo.Dataset;
+import org.tribuo.FeatureSelector;
+import org.tribuo.ImmutableFeatureMap;
+import org.tribuo.Model;
+import org.tribuo.SelectedFeatureSet;
+import org.tribuo.classification.Label;
+import org.tribuo.classification.ensemble.VotingCombiner;
+import org.tribuo.classification.evaluation.LabelEvaluation;
+import org.tribuo.classification.evaluation.LabelEvaluator;
+import org.tribuo.common.nearest.KNNModel;
+import org.tribuo.common.nearest.KNNTrainer;
+import org.tribuo.dataset.SelectedFeatureDataset;
+import org.tribuo.evaluation.CrossValidation;
+import org.tribuo.math.distance.L1Distance;
+import org.tribuo.math.neighbour.NeighboursQueryFactoryType;
+import org.tribuo.provenance.FeatureSelectorProvenance;
+import org.tribuo.provenance.FeatureSetProvenance;
+import org.tribuo.provenance.impl.FeatureSelectorProvenanceImpl;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Random;
+import java.util.SplittableRandom;
+import java.util.concurrent.ThreadLocalRandom;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+
+/**
+ * Select features based on Cuckoo Search algorithm with binary transfer functions, KNN classifier and 10-fold cross validation
+ * <p>
+ * see:
+ * <pre>
+ * Xin-She Yang and Suash Deb.
+ * "Cuckoo Search via L´evy Flights", 2010.
+ *
+ * L. A. M. Pereira et al.
+ * "A Binary Cuckoo Search and its Application for Feature Selection", 2014.
+ * </pre>
+ */
+public  final class CuckooSearchOptimizer implements FeatureSelector<Label> {
+    private final Trainer<Label> trainer;
+    private final TransferFunction transferFunction;
+    private final int populationSize;
+    private final double stepSizeScaling;
+    private final double lambda;
+    private final double worstNestProbability;
+    private final double delta;
+    private final double mutationRate;
+    private int [][] setOfSolutions;
+    private final int maxIteration;
+    private final SplittableRandom rng;
+    private final int seed;
+
+    /**
+     * The default constructor for feature selection based on Cuckoo Search Algorithm
+     */
+    public CuckooSearchOptimizer() {
+        this.trainer =  new KNNTrainer<>(1, new L1Distance(), Runtime.getRuntime().availableProcessors(), new VotingCombiner(), KNNModel.Backend.THREADPOOL, NeighboursQueryFactoryType.BRUTE_FORCE);
+        this.transferFunction = TransferFunction.V2;
+        this.populationSize = 50;
+        this.stepSizeScaling = 2d;
+        this.lambda = 2d;
+        this.worstNestProbability = 0.1d;
+        this.delta = 1.5d;
+        this.mutationRate = 0.2d;
+        this.maxIteration = 3;
+        this.seed = 12345;
+        this.rng = new SplittableRandom(seed);
+    }
+
+    /**
+     * Constructs the wrapper feature selection based on cuckoo search algorithm
+     * @param trainer The used trainer in the evaluation process
+     * @param transferFunction The transfer function to convert continuous values to binary ones
+     * @param populationSize The size of the solution in the initial population
+     * @param maxIteration The number of times that is used to enhance generation
+     * @param seed This seed is required for the SplittableRandom
+     */
+    public CuckooSearchOptimizer(Trainer<Label> trainer, TransferFunction transferFunction, int populationSize, int maxIteration, int seed) {
+        this.trainer = trainer;
+        this.transferFunction = transferFunction;
+        this.populationSize = populationSize;
+        this.stepSizeScaling = 2d;
+        this.lambda = 2d;
+        this.worstNestProbability = 1.5d;
+        this.delta = 1.5d;
+        this.mutationRate = 0.2d;
+        this.maxIteration = maxIteration;
+        this.seed = seed;
+        this.rng = new SplittableRandom(seed);
+    }
+
+    /**
+     * Constructs the wrapper feature selection based on cuckoo search algorithm
+     * @param trainer The used trainer in the evaluation process
+     * @param transferFunction The transfer function to convert continuous values to binary ones
+     * @param populationSize The size of the solution in the initial population
+     * @param stepSizeScaling The cuckoo step size
+     * @param lambda The lambda of the levy flight function
+     * @param worstNestProbability The fraction of the nests to be abandoned
+     * @param delta The delta that is used in the abandon nest function
+     * @param mutationRate The proportion to apply the mutation operator
+     * @param maxIteration The number of times that is used to enhance generation
+     * @param seed This seed is required for the SplittableRandom
+     */
+    public CuckooSearchOptimizer(Trainer<Label> trainer, TransferFunction transferFunction, int populationSize, double stepSizeScaling, double lambda, double worstNestProbability, double delta, double mutationRate, int maxIteration, int seed) {
+        this.trainer = trainer;
+        this.transferFunction = transferFunction;
+        this.populationSize = populationSize;
+        this.stepSizeScaling = stepSizeScaling;
+        this.lambda = lambda;
+        this.worstNestProbability = worstNestProbability;
+        this.delta = delta;
+        this.mutationRate = mutationRate;
+        this.maxIteration = maxIteration;
+        this.seed = seed;
+        this.rng = new SplittableRandom(seed);
+    }
+
+    /**
+     * This method is used to generate the initial population (set of solutions)
+     * @param totalNumberOfFeatures The number of features in the given dataset
+     * @return The population of subsets of selected features
+     */
+    private int[][] GeneratePopulation(int totalNumberOfFeatures) {
+        setOfSolutions = new int[this.populationSize][totalNumberOfFeatures];
+        for (int[] subSet : setOfSolutions) {
+            int[] values = new int[subSet.length];
+            for (int i = 0; i < values.length; i++) {
+                values[i] = rng.nextInt(2);
+            }
+            System.arraycopy(values, 0, subSet, 0, setOfSolutions[0].length);
+        }
+        return setOfSolutions;
+    }
+
+    /**
+     * Does this feature selection algorithm return an ordered feature set?
+     * @return True if the set is ordered.
+     */
+    @Override
+    public boolean isOrdered() {
+        return true;
+    }
+
+    /**
+     * Selects features according to this selection algorithm from the specified dataset.
+     * @param dataset The dataset to use.
+     * @return A selected feature set.
+     */
+    @Override
+    public SelectedFeatureSet select(Dataset<Label> dataset) {
+        ImmutableFeatureMap FMap = new ImmutableFeatureMap(dataset.getFeatureMap());
+        setOfSolutions = generatePopulation(FMap.size());
+        List<CuckooSearchFeatureSet> subSet_fScores = new ArrayList<>();
+        SelectedFeatureSet selectedFeatureSet;
+        for (int iter = 0; iter < maxIteration; iter++) {
+            for (int solution = 0; solution < setOfSolutions.length; solution++) {
+                int[] evolvedSolution = new int[setOfSolutions[0].length];
+                // Update the solution based on the levy flight function
+                for (int i = 0; i < setOfSolutions[0].length; i++) {
+                    evolvedSolution[i] = (int) transferFunction.applyAsDouble(setOfSolutions[solution][i] + stepSizeScaling * Math.pow(solution + 1, -lambda));
+                }
+                int randomCuckooIndex = rng.nextInt(setOfSolutions.length);
+                System.arraycopy(retrieveBestAfterEvaluation(dataset, FMap, evolvedSolution, setOfSolutions[randomCuckooIndex]), 0, setOfSolutions[randomCuckooIndex], 0, setOfSolutions[randomCuckooIndex].length);
+                // Update the solution based on the abandone nest function
+                if (new Random().nextDouble() < worstNestProbability) {
+                    int r1 = rng.nextInt(setOfSolutions.length);
+                    int r2 = rng.nextInt(setOfSolutions.length);
+                    for (int j = 0; j < setOfSolutions[0].length; j++) {
+                        evolvedSolution[j] = (int) transferFunction.applyAsDouble(setOfSolutions[solution][j] + delta * (setOfSolutions[r1][j] - setOfSolutions[r2][j]));
+                    }
+                    System.arraycopy(retrieveBestAfterEvaluation(dataset, FMap, evolvedSolution, setOfSolutions[solution]), 0, setOfSolutions[solution], 0, setOfSolutions[solution].length);
+                }
+                // Update the solution based on mutation operator
+                int[] mutedSolution = mutation(setOfSolutions[subSet.get()]);
+                System.arraycopy(retrieveBestAfterEvaluation(dataset, FMap, mutedSolution, setOfSolutions[solution]), 0, setOfSolutions[solution], 0, setOfSolutions[solution].length);
+                // Update the solution based on inversion mutation
+                mutedSolution = inversionMutation(setOfSolutions[subSet.get()]);
+                System.arraycopy(retrieveBestAfterEvaluation(dataset, FMap, mutedSolution, setOfSolutions[solution]), 0, setOfSolutions[solution], 0, setOfSolutions[solution].length);
+                // Update the solution based on swapped mutation
+                mutedSolution = swappedMutation(setOfSolutions[subSet.get()]);
+                System.arraycopy(retrieveBestAfterEvaluation(dataset, FMap, mutedSolution, setOfSolutions[solution]), 0, setOfSolutions[solution], 0, setOfSolutions[solution].length);
+                // Updata the solution based on Jaya operator
+                int[] jayaSolution = jayaOperator(setOfSolutions[subSet.get()], subSet_fScores.get(0).subSet(), subSet_fScores.get(subSet_fScores.size() - 1).subSet());
+                System.arraycopy(retrieveBestAfterEvaluation(dataset, FMap, jayaSolution, setOfSolutions[solution]), 0, setOfSolutions[solution], 0, setOfSolutions[solution].length);
+            }
+            Arrays.stream(setOfSolutions).map(subSet -> new CuckooSearchFeatureSet(subSet, FN.EvaluateSolution(this, dataset, FMap, subSet))).forEach(subSet_fScores::add);
+        }
+        subSet_fScores.sort(Comparator.comparing(CuckooSearchFeatureSet::score).reversed());
+        selectedFeatureSet = FN.getSFS(this, dataset, FMap, subSet_fScores.get(0).subSet);
+        return selectedFeatureSet;
+    }
+
+    @Override
+    public FeatureSelectorProvenance getProvenance() {
+        return new FeatureSelectorProvenanceImpl(this);
+    }
+
+    /**
+     * This method is used to compute the fitness score of each solution of the population
+     * @param optimizer The optimizer that is used for FS
+     * @param trainer The used trainer in the evaluation process
+     * @param dataset The dataset to use
+     * @param Fmap The dataset feature map
+     * @param solution The current subset of features
+     * @return The fitness score of the given subset
+     */
+    private  <T extends FeatureSelector<Label>> double evaluateSolution(T optimizer, Trainer<Label> trainer, Dataset<Label> dataset, ImmutableFeatureMap Fmap, int... solution) {
+        SelectedFeatureDataset<Label> selectedFeatureDataset = new SelectedFeatureDataset<>(dataset,getSFS(optimizer, dataset, Fmap, solution));
+        CrossValidation<Label, LabelEvaluation> crossValidation = new CrossValidation<>(trainer, selectedFeatureDataset, new LabelEvaluator(), 10);
+        double avgAccuracy = 0d;
+        for (Pair<LabelEvaluation, Model<Label>> ACC : crossValidation.evaluate()) {
+            avgAccuracy += ACC.getA().accuracy();
+        }
+        avgAccuracy /= crossValidation.getK();
+
+        return avgAccuracy + 0.001 * (1 - ((double) selectedFeatureDataset.getSelectedFeatures().size() / Fmap.size()));
+    }
+
+    /**
+     * This methid is used to return the selected subset of features
+     * @param optimizer The optimizer that is used for FS
+     * @param dataset The dataset to use
+     * @param featureMap The dataset feature map
+     * @param solution The current subset of featurs
+     * @return The selected feature set
+     */
+    private  <T extends FeatureSelector<Label>> SelectedFeatureSet getSFS(T optimizer, Dataset<Label> dataset, ImmutableFeatureMap featureMap, int... solution) {
+        List<String> names = new ArrayList<>();
+        List<Double> scores = new ArrayList<>();
+        for (int i = 0; i < solution.length; i++) {
+            if (solution[i] == 1) {
+                names.add(featureMap.get(i).getName());
+                scores.add(1d);
+            }
+        }
+        FeatureSetProvenance provenance = new FeatureSetProvenance(SelectedFeatureSet.class.getName(), dataset.getProvenance(), optimizer.getProvenance());
+
+        return new SelectedFeatureSet(names, scores, optimizer.isOrdered(), provenance);
+    }
+
+    /**
+     * @param dataset The dataset to use
+     * @param trainer The used trainer in the evaluation process
+     * @param FMap The map of selected features
+     * @param alteredSolution The modified solution
+     * @param oldSolution The old solution
+     */
+   public int[] retrieveBestAfterEvaluation(Dataset<Label> dataset, ImmutableFeatureMap FMap, int[] alteredSolution, int... oldSolution) {
+        if (FN.EvaluateSolution(this, dataset, FMap, alteredSolution) > FN.EvaluateSolution(this, dataset, FMap, oldSolution)) {
+            return alteredSolution;
+        }
+        else
+            return oldSolution;
+    }
+
+    /**
+     * The simple mutation method of Genetic algorithm
+     * <p>
+     * see:
+     * <pre>
+     * Steven Bayer and Lui Wang.
+     * "A Genetic Algorithm Programming Environment: Splicer", 1991.
+     * </pre>
+     * @param currentSolution The solution to be altered by the mutation operator
+     * @return The altered solution after mutation
+     */
+    private int[] mutation(int... currentSolution) {
+        return Arrays.stream(currentSolution).map(x -> ThreadLocalRandom.current().nextDouble() < mutationRate ? 1 - x : x).toArray();
+    }
+
+    /**
+     * The inversion mutation
+     * <p>
+     * see:
+     * <pre>
+     * Nitashs Soni and Tapsa Kumar.
+     * "Study of Various Mutation Operators in Genetic Algorithms", 2014.
+     * </pre>
+     * @param currentSolution The solution to be altered by the mutation operator
+     * @return The altered solution after inversion mutation
+     */
+    private int[] inversionMutation(int... currentSolution) {
+        int[] solution = new int[currentSolution.length];
+        System.arraycopy(currentSolution, 0, solution, 0, solution.length);
+        int rand1 = new Random().nextInt(solution.length);
+        int rand2 = new Random().nextInt(solution.length);
+        while (rand1 >= rand2) {
+            rand1 = new Random().nextInt(solution.length);
+            rand2 = new Random().nextInt(solution.length);
+        }
+        for (; rand1 < rand2; rand1++) {
+            solution[rand1] = 1 - solution[rand1];
+        }
+        return solution;
+    }
+
+    /**
+     * Sswapped mutation
+     * <p>
+     * see:
+     * <pre>
+     * Ming-Wen Tsai et al.
+     * "A Two-Dimensional Genetic Algorithm and Its Application to Aircraft Scheduling Problem", 2015.
+     * </pre>
+     * @param currentSolution The solution to be altered by the mutation operator
+     * @return The altered solution after swapped mutation
+     */
+    private int[] swappedMutation(int... currentSolution) {
+        int[] solution = new int[currentSolution.length];
+        System.arraycopy(currentSolution, 0, solution, 0, solution.length);
+        int firstGeneIndex = new Random().nextInt(currentSolution.length);
+        int secondGeneIndex = new Random().nextInt(currentSolution.length);
+        int secondGene = solution[secondGeneIndex];
+        solution[secondGeneIndex] = solution[firstGeneIndex];
+        solution[firstGeneIndex] = secondGene;
+        return solution;
+    }
+
+    /**
+     * The main equation of Jaya optimization algorithm
+     * <p>
+     * see:
+     * <pre>
+     * Venkata Rao.
+     * "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems", 2016.
+     * </pre>
+     * @param currentSolution The solution to be altered by the jaya operator
+     * @param currentBest The best solution in the current generation
+     * @param currentWorst The worst solution in the current generation
+     * @return The altered solution after appling jaya operator
+     */
+    private int[] jayaOperator(int[] currentSolution, int[] currentBest, int[] currentWorst) {
+        int[] newSolution = new int[currentSolution.length];
+        Arrays.setAll(newSolution, i -> (int) transferFunction.applyAsDouble(currentSolution[i] + new Random().nextDouble() * (currentBest[i] - currentSolution[i]) - new Random().nextDouble() * (currentWorst[i] - currentSolution[i])));
+        return newSolution;
+    }
+
+    /**
+     * This record is used to hold subset of features with its corresponding fitness score
+     */
+    record CuckooSearchFeatureSet(int[] subSet, double score) { }
+}