This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.

Genetic algorithms and Evolutionary computing - Project on the Travelling Salesman Problem (KU Leuven course H02D1A)

Competitive C++ solution to the Travelling Salesperson 2D problem, that includes the implementation of 6 algorithms: greedy, Clarke-Wright, Christofides, 2-opt, 3-opt, and Lin-Kernighan (k-opt). Done as part of the project assignment in the *DD22440 Advanced Algorithms* course at KTH, by Prof. Danupon Nanongkai.

Algorithmia: Backtracking, Prefix Sums problems, Multiple Knapsack problem (Dinamyc programing). Adapted BFS, Kruskal, Dijkstra implementations. Heuristics, Greedy algorithms, k-opt search.

Trabalho realizado para a disciplina Pesquisa Operacional em 2022.1. O Objetivo foi utilizar algoritmos vistos em aula em problemas NP-completos baseando-se em algum artigo da literatura.

This repository offers solutions to the Traveling Salesman Problem through two algorithms: Dynamic Programming for precise results and the K-opt Heuristic for fast, near-optimal routes. Whether you prioritize accuracy or speed, our codebase simplifies TSP optimization for various applications.