This paper presents a novel Genetic Parallel
Programming (GPP) paradigm for evolving parallel
programs running on a Multi-Arithmetic-Logic-Unit
(Multi-ALU) Processor (MAP). The MAP is a Multiple
Instruction-streams, Multiple Data-streams (MIMD),
general-purpose register machine that can be
implemented on modern Very Large-Scale Integrated
Circuits (VLSIs) in order to evaluate genetic programs
at high speed. For human programmers, writing parallel
programs is more difficult than writing sequential
programs. However, experimental results show that GPP
evolves parallel programs with less computational
effort than that of their sequential counterparts. It
creates a new approach to evolving a feasible problem
solution in parallel program form and then serialises
it into a sequential program if required. The
effectiveness and efficiency of GPP are investigated
using a suite of 14 well-studied benchmark problems.
Experimental results show that GPP speeds up evolution
substantially.
%0 Journal Article
%1 Cheang:2006:EC
%A Cheang, Sin Man
%A Leung, Kwong Sak
%A Lee, Kin Hong
%D 2006
%J Evolutionary Computation
%K ALU GPP MAP, MIMD, algorithms, architecture, genetic linear parallel processor programming,
%N 2
%P 129--156
%R doi:10.1162/evco.2006.14.2.129
%T Genetic Parallel Programming: Design and
Implementation
%V 14
%X This paper presents a novel Genetic Parallel
Programming (GPP) paradigm for evolving parallel
programs running on a Multi-Arithmetic-Logic-Unit
(Multi-ALU) Processor (MAP). The MAP is a Multiple
Instruction-streams, Multiple Data-streams (MIMD),
general-purpose register machine that can be
implemented on modern Very Large-Scale Integrated
Circuits (VLSIs) in order to evaluate genetic programs
at high speed. For human programmers, writing parallel
programs is more difficult than writing sequential
programs. However, experimental results show that GPP
evolves parallel programs with less computational
effort than that of their sequential counterparts. It
creates a new approach to evolving a feasible problem
solution in parallel program form and then serialises
it into a sequential program if required. The
effectiveness and efficiency of GPP are investigated
using a suite of 14 well-studied benchmark problems.
Experimental results show that GPP speeds up evolution
substantially.
@article{Cheang:2006:EC,
abstract = {This paper presents a novel Genetic Parallel
Programming (GPP) paradigm for evolving parallel
programs running on a Multi-Arithmetic-Logic-Unit
(Multi-ALU) Processor (MAP). The MAP is a Multiple
Instruction-streams, Multiple Data-streams (MIMD),
general-purpose register machine that can be
implemented on modern Very Large-Scale Integrated
Circuits (VLSIs) in order to evaluate genetic programs
at high speed. For human programmers, writing parallel
programs is more difficult than writing sequential
programs. However, experimental results show that GPP
evolves parallel programs with less computational
effort than that of their sequential counterparts. It
creates a new approach to evolving a feasible problem
solution in parallel program form and then serialises
it into a sequential program if required. The
effectiveness and efficiency of GPP are investigated
using a suite of 14 well-studied benchmark problems.
Experimental results show that GPP speeds up evolution
substantially.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Cheang, Sin Man and Leung, Kwong Sak and Lee, Kin Hong},
biburl = {https://www.bibsonomy.org/bibtex/295b10b0a77a3bf98bd8b0cc7f63f6dda/brazovayeye},
doi = {doi:10.1162/evco.2006.14.2.129},
interhash = {8f0f9d4e4e1165026cfb0d450b887fbf},
intrahash = {95b10b0a77a3bf98bd8b0cc7f63f6dda},
issn = {1063-6560},
journal = {Evolutionary Computation},
keywords = {ALU GPP MAP, MIMD, algorithms, architecture, genetic linear parallel processor programming,},
month = {Summer},
number = 2,
pages = {129--156},
size = {28 pages},
timestamp = {2008-06-19T17:37:36.000+0200},
title = {Genetic Parallel Programming: Design and
Implementation},
volume = 14,
year = 2006
}