About: Parallelism and partitioning in large-scale GAs using spark

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Parallelism and partitioning in large-scale GAs using spark Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : linkeddata.uriburner.com:28898 associated with source document(s)

Attributes	Values
type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #75345 Parallelism and partitioning in large-scale GAs using spark
sameAs	Parallelism and partitioning in large-scale GAs using spark
http://eprints.org/ontology/hasAccepted	Parallelism and partitioning in large-scale GAs using spark (PDF)
http://eprints.org/ontology/hasDocument	Parallelism and partitioning in large-scale GAs using spark (PDF) Parallelism and partitioning in large-scale GAs using spark (Other) Parallelism and partitioning in large-scale GAs using spark (Other) Parallelism and partitioning in large-scale GAs using spark (Other) Parallelism and partitioning in large-scale GAs using spark (Other) Parallelism and partitioning in large-scale GAs using spark (Other)
dc:hasVersion	Parallelism and partitioning in large-scale GAs using spark (PDF)
Title	Parallelism and partitioning in large-scale GAs using spark
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-07-13
Creator	Laila Alterkawi Matteo Migliavacca
status	peer reviewed published
Publisher	ACM
abstract	Big Data promises new scientific discovery and economic value. Genetic algorithms (GAs) have proven their flexibility in many application areas and substantial research effort has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts we reject approaches that are based on the centralisation of data in the main memory of a single node or that require remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines. In this partitioned scenario, we explore two parallelisation models: PDMS, inspired by the traditional master-slave model, and PDMD, based on island models; we compare their performance in large-scale classification problems. We implement two distributed versions of Bio-HEL, a popular large-scale single-node GA classifier, using the Spark distributed data processing platform. In contrast to existing GA based on MapReduce, Spark allows a more efficient implementation of parallel GAs thanks to its simple, efficient iterative processing of partitioned datasets. We study the accuracy, efficiency and scalability of the proposed models. Our results show that PDMS provides the same accuracy of traditional BioHEL and exhibit good scalability up to 64 cores, while PDMD provides substantial reduction of execution time at a minor loss of accuracy.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	QA76.76 Computer software
list of authors	https://kar.kent.ac.uk/id/eprint/75345#authors
presented at	Genetic and Evolutionary Computation Conference (GECCO 2019)
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #75345 Parallelism and partitioning in large-scale GAs using spark

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 12 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software