Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7649
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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:Applied Data Science - Publication
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