Synthetic Depth Data Generation Using Simulated Annealing (on Body Tracking Modality)

Morph an input dataset of 2D points into select shapes, while preserving the summary statistics to a given number of decimal points through simulated annealing. It is intended to be used as a teaching tool to illustrate the importance of data visualization.

The game Tetris gets played by AI, fully automatic.

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

Flappy Bird, but with Reinforced Learning.

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.

This repository implements a hybrid algorithm to solve the Traveling Salesman Problem (TSP), combining Genetic Algorithms (GA) with Simulated Annealing (SA) for optimization. The objective is to find the shortest possible route that visits a set of cities exactly once and returns to the origin city.

In this repo, GA, SA, and PSO algorithms are implemented to solve Job-shop Scheduling Problem(JSP) and Flexible Job-shop Scheduling Problem (FJSP) problem.

Implementation and Evaluation of "Genetic" and "Simulated Annealing" algorithms for Extended version of Travelling Salesman Problem.

Framework de algoritmos para TSP

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.

The aim of this Python project is to circularize a plasmid by optimizing the values of a table calculating the plasmid's 3D trajectory. To achieve this, 2 algorithms are used and compared: simulated annealing and genetic algorithm.

Multi-objective Simulated Annealing (MOSA) implementation in pure Python.

Python Implementation of Traveling Salesman Problem (TSP) Using Genetic Algorithms/Hybridized with more Heuristic Optimizations

Python REST client and examples for DNA-Evolutions TourOptimizer

An MDL based learning framework for head-complement order using minimalist grammars.

Optimization algorithms, including Simulated Annealing (SA) and Biased Random-Key Genetic Algorithm (BRKGA) for the multiple Traveling Salesman Problem (mTSP).

Travelling Salesman Problem (TSP) using Simulated Annealing

AI - Project 2 - This project implements Tabu Search and Simulated Annealing to produce optimal solution of the TSP.

This project applies Simulated Annealing to solve the Traveling Salesman Problem using Peru's departments as nodes. Through iterative refinement, it finds the shortest route visiting each department once. Visual feedback enhances understanding and debugging, resulting in an optimal solution displayed with total distance.