NDLI: Parallel Genetic Algorithm based on a new migration strategy

Content Provider	IEEE Xplore Digital Library
Author	Falahiazar, L. Teshnehlab, M. Falahiazar, A.
Copyright Year	2012
Description	Author affiliation: Department of computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran (Falahiazar, L.; Teshnehlab, M.; Falahiazar, A.)
Abstract	Parallel Genetic Algorithm (PGA) is used in many practical global optimizations to achieve high speed in convergence. The Island Model Parallel Genetic Algorithm (IMPGA) is very useful. Genetic Algorithms are one of the most powerful search and optimization method when we must solvecomplex and time consuming problems. IMPGA are more flexible than other PGA methods. There are several variables in the IMPGA that determining them are effective to enhance performance of the IMPGA. In this paper, we proposed a Migration method (Max-Min method). In our proposed method, according to status of subpopulation and comparing subpopulation with other subpopulations, the individuals for migrationare selected. In addition to enhancing the performance of PGA, we propose another method that embedding Hill-Climbing Algorithm within the structure of the PGA. As we know, creating an optimized structure for a Neural Network is a time consuming problem and costly one. The problem was studied in this paper is to determine the structure of a Neural Network forforecasting next day air quality. In addition, we used real data which was received from the Meteorological Organization and Tehran's Air Pollution Company. Output of the neural network is the value of Ozone Gas (o3) for the next 24 hours. The results of our two proposed methods are compared with conventional methods in other papers. Our algorithm has better performance than other papers.
Starting Page	37
Ending Page	41
File Size	645280
Page Count	5
File Format	PDF
ISBN	9781467302524
e-ISBN	9781467302555
DOI	10.1109/RACSS.2012.6212694
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-25
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural Network Parallel Genetic Algorithm Neural networks Genetic Algorithm Topology Biological cells Optimization Electronics packaging Genetic algorithms Convergence Island Model Parallel Genetic Algorithm
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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