%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 }