chore: initialize lab3 folder, code and report

lab3/EARIN_LAB_3_RUDNICKI_KLISZKO.tex

@@ -0,0 +1,14 @@
+\documentclass{article}
+\usepackage{graphicx} % Required for inserting images
+
+\title{EARIN_LAB_3_RUDNICKI_KLISZKO.tex}
+\author{321krzychu }
+\date{April 2023}
+
+\begin{document}
+
+\maketitle
+
+\section{Introduction}
+
+\end{document}

lab3/main.py

@@ -0,0 +1,57 @@
+"""
+Program that optimizes Rastrigin function: f (x, y) = 
+20 + (x^2 - 10cos(2πx)) + (y^2 - 10 cos(2πy)).
+Using Evolutionary Strategy (μ, λ).
+"""
+import numpy as np
+
+
+def rastrigin(x_argument, y_argument):
+    """ Define the Rastrigin function """
+    return 20 + x_argument**2 - 10 * np.cos(2 * np.pi * x_argument) + \
+        y_argument**2 - 10 * np.cos(2 * np.pi * y_argument)
+
+
+def evolution_strategy(top_individuals, lambda_, sigma, iterations):
+    """ Define the Evolutionary Strategy (μ, λ) algorithm """
+    # Initialize the population
+    population = np.random.uniform(low=-5.12, high=5.12, size=(lambda_, 2))
+
+    # Iterate for a fixed number of iterations
+    for i in range(iterations):
+        # Evaluate the fitness of each individual
+        fitness = np.array([rastrigin(x, y) for x, y in population])
+
+        # Select the top top_individuals individuals
+        parents = population[np.argsort(fitness)[:top_individuals]]
+
+        # Generate the next generation of lambda individuals by recombination
+        children = np.concatenate(
+            [np.random.permutation(parents) for i in range(lambda_)])
+
+        # Add mutation to the children
+        mutation = np.random.normal(loc=0, scale=sigma, size=(lambda_, 2))
+        population = children + mutation
+
+    # Evaluate the fitness of the final population
+    fitness = np.array([rastrigin(x, y) for x, y in population])
+
+    # Return the best individual found
+    best_idx = np.argmin(fitness)
+    return population[best_idx], fitness[best_idx]
+
+
+# Ran first in the code
+if __name__ == "__main__":
+    # Set the parameters
+    MU = 5
+    LAMBDA = 20
+    SIGMA = 0.1
+    ITERATIONS = 100
+
+    # Run the Evolutionary Strategy algorithm
+    best_individual, best_fitness = evolution_strategy(
+        MU, LAMBDA, SIGMA, ITERATIONS)
+
+    print("Best individual found:", best_individual)
+    print("Best fitness found:", best_fitness)

main.py

@@ -1 +1 @@
-print("I lost the game")
+print("I lost the game")