Open AccessAspects of the Study of Genetic Algorithms and Mechanisms for their Optimization for the Travelling Salesman Problem
Author(s) -
Nataliya Boyko,
Andriy Pytel
Publication year - 2021
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.20.4.2442
Subject(s) - travelling salesman problem , computer science , genetic algorithm , 2 opt , lin–kernighan heuristic , artificial neural network , selection (genetic algorithm) , mathematical optimization , process (computing) , artificial intelligence , algorithm , machine learning , mathematics , operating system
Lately, artificial intelligence has become increasingly popular. Still, at the same time, a stereotype has been formed that AI is based solely on neural networks, even though a neural network is only one of the numerous directions of artificial intelligence. This paper aims to bring attention to other directions of AI, such as genetic algorithms. In this paper, we study the process of solving the travelling salesman problem (TSP) via genetic algorithms (GA) and consider the issues of this method. The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that are based on natural selection, the process that drives biological evolution. One of the common problems in programming is the travelling salesman problem. Many methods can be used to solve it, but we are going consider genetic algorithms. This study aims at developing the most efficient application of genetic algorithms in the travelling salesman problem.