A Powerful Genetic Algorithm for Traveling Salesman Problem

2014/02/19 by Shujia Liu, Liu, Shujia · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Vehicle Routing Optimization Methods

paper · pdf · doi:10.48550/arxiv.1402.4699

openalex publication_date 2014/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents a powerful genetic algorithm(GA) to solve the traveling salesman problem (TSP). To construct a powerful GA, I use edge swapping(ES) with a local search procedure to determine good combinations of building blocks of parent solutions for generating even better offspring solutions. Experimental results on well studied TSP benchmarks demonstrate that the proposed GA is competitive in finding very high quality solutions on instances with up to 16,862 cities.

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• Resource-efficient variational quantum solver for the travelling salesman problem and its silicon photonics implementation

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