Web-Based Implementation of Travelling Salesperson Problem Using Genetic Algorithm

TL;DR

This paper explores a web-based implementation of the Traveling Salesperson Problem using Genetic Algorithms, benchmarking PHP, Ruby, and Python to assess performance and code efficiency.

Contribution

It compares the performance of different web-based programming languages in implementing GA for TSP, highlighting Ruby's advantages.

Findings

Ruby outperforms PHP and Python in runtime efficiency

Code size and file size vary significantly among languages

Ruby is recommended for GA web-based implementation

Abstract

The world is connected through the Internet. As the abundance of Internet users connected into the Web and the popularity of cloud computing research, the need of Artificial Intelligence (AI) is demanding. In this research, Genetic Algorithm (GA) as AI optimization method through natural selection and genetic evolution is utilized. There are many applications of GA such as web mining, load balancing, routing, and scheduling or web service selection. Hence, it is a challenging task to discover whether the code mainly server side and web based language technology affects the performance of GA. Travelling Salesperson Problem (TSP) as Non Polynomial-hard (NP-hard) problem is provided to be a problem domain to be solved by GA. While many scientists prefer Python in GA implementation, another popular high-level interpreter programming language such as PHP (PHP Hypertext Preprocessor) and Ruby were benchmarked. Line of codes, file sizes, and performances based on GA implementation and runtime were found varies among these programming languages. Based on the result, the use of Ruby in GA implementation is recommended.