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

Author

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

fYear

2015

fDate

24-27 May 2015

Firstpage

790

Lastpage

793

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).

Keywords

application program interfaces; classification; data mining; drugs; genetic algorithms; medical computing; medical information systems; message passing; parallel algorithms; PC cluster; biomedical application; computation time; medical drug classification; message passing interface; minimal reduct computation; node variation; parallel distributed genetic algorithm; sequential method; side effect set; variable mutation rate; Biological cells; Drugs; Genetic algorithms; Optimization; Set theory; Sociology; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (ISCAS), 2015 IEEE International Symposium on

Conference_Location

Lisbon

Type

conf

DOI

10.1109/ISCAS.2015.7168752

Filename

7168752