Local Search to solve the 8-Queens puzzle. Implemented Algorithms: Hill Climbing: Steepest Ascent, First Choice and Simulated Annealing.

Implémentation d'algorithmes de Résolution de Problèmes

code to scrape google maps results for tracking local seo rank

Prácticas de la asignatura Metaheurísticas (MH) impartidas en la Universidad de Granada durante el curso académico 2019/2020

A coding project aimed at exploring new ways of algorithmic learning using evolutionary techniques such as genetic algorithms and crossover

Bag problem solver using genetic algorithms and local search algorithms.

Implementation of a genetic algorithm to solve the travelingsalesman problem.

Python implementation of heuristics for the TSP.

Simulation Tools and Techniques - Project - Traveling Umpire Problem

Implementation of constraint satisfaction problem algorithms to solve the radio link frequency assignment problem - rlfap

A computational intelligence project exploring metaheuristic algorithms for combinatorial optimization, developed for the Artificial Intelligence course at FEUP.

This repository contains the code and experiments related to the final project for the course Bio-Inspired Artificial Intelligence at the University of Trento.

Heuristic optimization work for university. Optimize the ordering of task. By using optimization algorithm and methods. Like the local search hillclimbing etc.

Some scripts for the Reaktor Traveling Santa competition

Python-based prototype for the optimization of the incoming orders to a logistic center.

Implementation of a generalized Constraint Satisfaction Problem, alongside a backtracking solver with MAC, min-remaining-value, and least-constraining-value heuristics. Applied to map coloring, the N-Queens Problem, and Circuit Board design. Also an implementation of a min-conflicts local search that is ideal for the N-Queens.

Implementation and comparison of a number of different approaches for solving a resource assignment scheduling problem.

This repository seeks to optimize bikes distribution of a public bicycle renting service across city stations using local search algorithms like Hill Climbing and Simulated Annealing, aiming to minimize costs and efficiently meet demand. It includes tools to visualize the distribution and showcases the utility of AI in urban logistics.

find the best meal with the lowest cost and lowest calories (Minimize the calories and the cost)

Evolutionary algorithms and metaheuristics project