Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster

Fazal Noor, Majed Alhaisoni, Mashaan A. Alshammari, Ravi P. Ramachandran

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    A Distributed Genetic Algorithm to compute minimal reducts is presented for a novel biomedical application to distinguish 50 medical drugs from 228 side effects. The results indicate that 15 side effects are sufficient to differentiate among all the 50 drugs. In fact, any one of 4 sets of 15 side effects can be used. The Distributed Genetic Algorithm is inherently parallel, uses a variable mutation rate and is efficiently implemented on a PC cluster using 5, 10 and 20 nodes each with a Message Passing Interface. Results show that the distributed algorithm with 20 nodes uses much less computation time than two sequential methods (savings of about a factor of 5).

    Original languageEnglish (US)
    Title of host publication2015 IEEE International Symposium on Circuits and Systems, ISCAS 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages790-793
    Number of pages4
    ISBN (Electronic)9781479983919
    DOIs
    StatePublished - Jul 27 2015
    EventIEEE International Symposium on Circuits and Systems, ISCAS 2015 - Lisbon, Portugal
    Duration: May 24 2015May 27 2015

    Publication series

    NameProceedings - IEEE International Symposium on Circuits and Systems
    Volume2015-July
    ISSN (Print)0271-4310

    Other

    OtherIEEE International Symposium on Circuits and Systems, ISCAS 2015
    CountryPortugal
    CityLisbon
    Period5/24/155/27/15

    All Science Journal Classification (ASJC) codes

    • Electrical and Electronic Engineering

    Fingerprint Dive into the research topics of 'Distinguishing medical drugs from a large set of side effects using a distributed genetic algorithm on a PC cluster'. Together they form a unique fingerprint.

    Cite this