In this project, I implemented an Evolutionary Algorithm (EA) to solve the Travelling Salesman Problem (TSP), a classic optimization challenge where the goal is to find the shortest route that visits a set of cities exactly once and returns to the starting point.

👩‍💻Text Decryption, Artificial Intelligence course, University of Tehran

A Genetic Algorithm project for solving The Traveling Salesman Problem "TSP"using Roulette Wheel Selection, Ordered Crossover (OX) and Mutation Swap Mutation

This project solves the GECCO19 Traveling Thief Problem (TTP) using a Multi-objective Evolutionary Algorithm (MOEA) to optimize both travel time (TSP) and profit (KNP) with advanced crossover, mutation, and selection operators

Implementation of the mutation operation "choose a gene at random" and the crossover operation "one-point crossover" for the Bin-Packing Problem

It is bioinformatics project

Deliverables relating to the Machine Learning Introduction and Classification University Unit

A Genetic Algorithm implementation for the Steiner Tree Problem in Graphs using GPX crossover operator.

A playground for writing crossover functions and see how they perform on the N-Queens problem!

A simple to use, ready to integrate genetic algorithm package

🧬Logic Gate Assignment, Artificial Intelligence course, University of Tehran

Solving the Traveling Sales Person problem using genetic algorithms.

Resolve TSP problem with GA and more crossover.

This program implements a genetic algorithm for curve fitting using a polynomial equation. The goal is to find the best coefficients for the polynomial equation that minimize the distance between the curve and a given set of data points. The genetic algorithm is used to search for the optimal solution by evolving a population of candidate solutions

This repository contains a C++ program that solves the Knapsack Problem using a Genetic Algorithm. The Knapsack Problem is a classic optimization problem where we aim to maximize the total value of items to be packed in a knapsack, given the knapsack's weight capacity and a set of items with their respective weights and values.