Example programs for usage of the Chips-n-Salsa library

The project is about solving symmetrical traveling salesman problem. The repository contains 4 optimization algorithms: Tabu Search, Hill Climbing with Multi-Start, Nearest Neighbor and Simulated Annealing.

A remake of the Flappy Bird game with Artificial Intelligence developed as a project for the Fondamenti di Intelligenza Artificiale (Fundamentals of Artificial Intelligence) course, part of the Computer Science Bachelor's Degree program at the University of Salerno

Implementation of metaheuristic optimization methods in Python for scientific, industrial, and educational scenarios. Experiments can be executed in parallel or in a distributed fashion. Experimental results can be evaluated in various ways, including diagrams, tables, and export to Excel.

A Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms

ANFIS PSO Curve Fitting Solver

Optimization includes a class of methods to find global or local optima for discrete or continuous objectives; from evolutionary-based algorithms to swarm-based ones.

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Final Project in AI Course, HUJI

Unity Game Engine is Used for game development, scene management, and physics simulation of Hill Climb Racing. C# is the primary programming language for game logic and scripting.

C++ symbolic regression core

An advanced route optimization tool with React that leverages metaheuristic algorithms to solve the Traveling Salesman Problem (TSP). Whether on a grid or a map.

FIB-IA 2022-23 Q2 Artificial Intelligence subject

An AI pathfinding project using the Hill Climbing algorithm to highlight the shortest path from any city to Bucharest on a Romania map, featuring a GUI.

Metaheuristic Algorithms for Bin Packing Problem (1D and 2D)

This project explores optimization algorithms to solve complex problems. It includes a hill-climbing algorithm, enhancements with random restarts, and a genetic algorithm. The project demonstrates implementation, testing, and visualization of these algorithms using Python. Ideal for learning and applying optimization techniques.

An attempt to create the perfect AI for the Snake game 🐍

The objective of this project is to solve a problem of a Distributed File System using two local search algorithms. Hill Climbing Simulated Annealing

Python3 code for gradient-free global optimization

This project was presented for the Artificial Intelligence course for the academic year 2022/2023. It explores various methods to solve the N-Queens problem, including Random Search, Backtracking, Hill-Climbing, Simulated Annealing, and Genetic Algorithms. Each method is evaluated for its efficiency and effectiveness in finding solutions.