Distributed genetic algorithm on graphX

Seemran Mishra, Young Choon Lee*, Abhaya Nayak

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

2 Citations (Scopus)


In this paper we explore the application of a recent breed of distributed systems, graph processing frameworks in particular, to solving complex research problems. These frameworks are designed to take full advantage of today’s abundant resources with their inherent distributed computing functionalities. Abstraction of many technical details, such as networking and coordination of multiple compute nodes is a desirable feature provided by these graph-processing frameworks. While these frameworks are largely used to process and analyse the web graphs and social networks, their capacity is not limited to this direct application. This paper is based on design and implementation of a genetic algorithm (GA) using a graph processing tool, GraphX for the task scheduling problem as a case study. Our experimental results show that GraphX can significantly aid in devising distributed solutions for complex problems.

Original languageEnglish
Title of host publicationAI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages7
Volume9992 LNAI
ISBN (Print)9783319501260
Publication statusPublished - 2016
Event29th Australasian Joint Conference on Artificial Intelligence, AI 2016 - Hobart, Australia
Duration: 5 Dec 20168 Dec 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9992 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Other29th Australasian Joint Conference on Artificial Intelligence, AI 2016


Dive into the research topics of 'Distributed genetic algorithm on graphX'. Together they form a unique fingerprint.

Cite this