diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..987df74
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,112 @@
+
+# Created by https://www.gitignore.io/api/python
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+bin/
+include/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule.*
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+.vscode/
+
+
+# End of https://www.gitignore.io/api/python
diff --git a/optimize_KNN_parameters/Pipfile b/optimize_KNN_parameters/Pipfile
new file mode 100644
index 0000000..b506259
--- /dev/null
+++ b/optimize_KNN_parameters/Pipfile
@@ -0,0 +1,24 @@
+[[source]]
+
+url = "https://pypi.python.org/simple"
+verify_ssl = true
+name = "pypi"
+
+
+[packages]
+
+niapy = "*"
+numpy = "*"
+scikit-learn = "*"
+pygal = "*"
+lxml = "*"
+cairosvg = "*"
+
+
+[dev-packages]
+
+
+
+[requires]
+
+python_version = "3.6"
diff --git a/optimize_KNN_parameters/Pipfile.lock b/optimize_KNN_parameters/Pipfile.lock
new file mode 100644
index 0000000..8d335a4
--- /dev/null
+++ b/optimize_KNN_parameters/Pipfile.lock
@@ -0,0 +1,311 @@
+{
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+            "platform_system": "Linux",
+            "platform_version": "#46~16.04.1-Ubuntu SMP Thu May 3 10:06:43 UTC 2018",
+            "python_full_version": "3.6.3",
+            "python_version": "3.6",
+            "sys_platform": "linux"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.6"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.python.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
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+            "version": "==0.6.1"
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+        "webencodings": {
+            "hashes": [
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+            "version": "==0.5.1"
+        },
+        "xlsxwriter": {
+            "hashes": [
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+            ],
+            "version": "==1.0.5"
+        }
+    },
+    "develop": {}
+}
diff --git a/optimize_KNN_parameters/chart.png b/optimize_KNN_parameters/chart.png
new file mode 100644
index 0000000..4f76e64
Binary files /dev/null and b/optimize_KNN_parameters/chart.png differ
diff --git a/optimize_KNN_parameters/run.py b/optimize_KNN_parameters/run.py
new file mode 100644
index 0000000..2a02e9e
--- /dev/null
+++ b/optimize_KNN_parameters/run.py
@@ -0,0 +1,117 @@
+import numpy as np
+from sklearn import datasets
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import StratifiedKFold
+from sklearn.model_selection import cross_validate
+from NiaPy.algorithms.modified import HybridBatAlgorithm
+import pygal 
+
+KNN_WEIGHT_FUNCTIONS = [
+    'uniform',
+    'distance'
+]
+
+KNN_ALGORITHMS = [
+    'ball_tree',
+    'kd_tree',
+    'brute'
+]
+
+# map from real number [0, 1] to integer ranging [5, 15]
+def swap_n_neighbors(val):
+    return int(val * 10 + 5)
+
+# map from real number [0, 1] to integer ranging [0, 1]
+def swap_weights(val):
+    if val > 0.5:
+        return 1
+    return 0
+
+# map from real number [0, 1] to integer ranging [1, 3]
+def swap_algorithm(val):
+    if val == 1:
+        return 3
+    return int(val * 3 + 1)
+
+# map from real number [0, 1] to integer ranging [10, 50]
+def swap_leaf_size(val):
+    return int(val * 10 + 40)
+
+class KNNBreastCancerBenchmark(object):
+    def __init__(self):
+        self.Lower = 0
+        self.Upper = 1
+    
+    def function(self):
+        # our definition of fitness function
+        def evaluate(D, solution):
+            n_neighbors = swap_n_neighbors(solution[0])
+            weights = KNN_WEIGHT_FUNCTIONS[(swap_weights(solution[1]) - 1)]
+            algorithm = KNN_ALGORITHMS[(swap_algorithm(solution[2]) - 1)]
+            leaf_size = swap_leaf_size(solution[3])
+
+            fitness = 1 - KNNBreastCancerClassifier(1234).evaluate(n_neighbors, weights, algorithm, leaf_size)
+            scores.append([fitness, n_neighbors, weights, algorithm, leaf_size])
+
+            return fitness
+        return evaluate
+
+class KNNBreastCancerClassifier(object):
+    def __init__(self, seed=1234):
+        self.seed = seed
+        self.ten_fold_scores = {}
+        self.default_ten_fold_scores = {}
+        np.random.seed(self.seed)
+
+        dataset = datasets.load_breast_cancer()
+        self.X = dataset.data
+        self.y = dataset.target
+        
+        self.X_search, self.X_validate, self.y_search, self.y_validate = train_test_split(self.X, self.y, test_size=0.8, random_state=self.seed)
+        self.X_search_train, self.X_search_test, self.y_search_train, self.y_search_test = train_test_split(self.X_search, self.y_search, test_size=0.8, random_state=self.seed)
+
+
+    def evaluate(self, n_neighbors, weights, algorithm, leaf_size):
+        model = KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm, leaf_size=leaf_size)
+        model.fit(self.X_search_train, self.y_search_train)
+        return model.score(self.X_search_test, self.y_search_test)
+
+    def run_10_fold(self, solution=None):
+        if solution is None:
+            estimator = KNeighborsClassifier()
+            kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=self.seed)
+            self.default_ten_fold_scores = cross_validate(estimator, self.X, self.y, cv=kfold, scoring=['accuracy'])
+        else:
+            estimator = KNeighborsClassifier(n_neighbors=solution[1], weights=solution[2], algorithm=solution[3], leaf_size=solution[4])
+            kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=self.seed)
+            self.ten_fold_scores = cross_validate(estimator, self.X_validate, self.y_validate, cv=kfold, scoring=['accuracy'])
+
+
+scores = []
+algorithm = HybridBatAlgorithm(4, 40, 100, 0.9, 0.1, 0.001, 0.9, 0.0, 2.0, KNNBreastCancerBenchmark())
+best = algorithm.run()
+
+print('Optimal KNN parameters are:')
+best_solution = []
+
+for score in scores:
+    if score[0] == best:
+        best_solution = score
+
+print(best_solution)
+
+model = KNNBreastCancerClassifier()
+model.run_10_fold(solution=best_solution)
+model.run_10_fold()
+
+print('best model mean test accuracy: ' + str(np.mean(model.ten_fold_scores['test_accuracy'])))
+print('default model mean test accuracy: ' + str(np.mean(model.default_ten_fold_scores['test_accuracy'])))
+
+box_plot = pygal.Box(width=800, height=600, range=(0.8, 0.99))
+box_plot.zero = 0.9
+box_plot.title = '10-fold cross-validation accuracies'
+box_plot.add('Best model', model.ten_fold_scores['test_accuracy'])
+box_plot.add('Default model', model.default_ten_fold_scores['test_accuracy'])
+
+box_plot.render_to_png('chart.png')
\ No newline at end of file