Studying parallel evolutionary algorithms: The cellular programming case

Mathieu Capcarrère, Andrea Tettamanzi, Marco Tomassini, Moshe Sipper

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

6 Scopus citations

Abstract

Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile-though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature, PPSN 1998 - 5th International Conference, Proceedings
PublisherSpringer Verlag
Pages573-582
Number of pages10
ISBN (Print)3540650784, 9783540650782
DOIs
StatePublished - 1 Jan 1998
Externally publishedYes
Event5th International Conference on Parallel Problem Solving from Nature, PPSN 1998 - Amsterdam, Netherlands
Duration: 27 Sep 199830 Sep 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1498 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Parallel Problem Solving from Nature, PPSN 1998
Country/TerritoryNetherlands
CityAmsterdam
Period27/09/9830/09/98

Fingerprint

Dive into the research topics of 'Studying parallel evolutionary algorithms: The cellular programming case'. Together they form a unique fingerprint.

Cite this