Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7649
DC FieldValueLanguage
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLee, Kin Hongen_US
dc.contributor.authorCheang, Sin Manen_US
dc.date.accessioned2023-03-29T04:59:36Z-
dc.date.available2023-03-29T04:59:36Z-
dc.date.issued2002-
dc.identifier.citationProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2002, vol. 2, pp. 1703 - 1708, Article number 1004499en_US
dc.identifier.isbn0780372824-
dc.identifier.isbn978-078037282-5-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7649-
dc.description.abstractThis paper proposes a novel genetic parallel programming (GPP) paradigm for evolving optimal parallel programs running on a multi-ALU processor by linear genetic programming. GPP uses a two-phase evolution approach. It evolves completely correct solution programs in the first phase. Then it optimizes execution speeds of solution programs in the second phase. Besides, GPP also employs a new genetic operation that swaps sub-instructions of a solution program. Three experiments (Sextic, Fibonacci and Factorial) are given as examples to show that GPP could discover novel parallel programs that fully utilize the processor's parallelism. © 2002 IEEE.en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.ispartofProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002en_US
dc.titleEvolving parallel machine programs for a multi-ALU processoren_US
dc.typeConference Proceedingsen_US
dc.identifier.doi10.1109/CEC.2002.1004499-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Publication
Show simple item record

SCOPUSTM   
Citations

14
checked on Jan 3, 2024

Page view(s)

21
checked on Jan 3, 2024

Google ScholarTM

Impact Indices

Altmetric

PlumX

Metrics


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.