NDLI: Adam-Eve genetic algorithm as a function optimizer

Content Provider	IEEE Xplore Digital Library
Author	Feyzbakhsh, S.A. Matsui, M.
Copyright Year	1999
Description	Author affiliation: Bus. Sch., Michigan Univ., Ann Arbor, MI, USA (Feyzbakhsh, S.A.)
Abstract	There are several different types of evolutionary algorithm (EA). Genetic algorithms (GAs) receive much attention as general-purpose methods for search and optimization. GAs which are powerful stochastic optimization techniques are perhaps the most widely known type of EAs. There are several methods to make GAs faster and our proposed Adam-Eve-like GA appears to be a promising methodology. Taking a hint from nature, the proposed method has some special characteristics which distinguish it from others. The Adam-Eve-like GA is discussed and its performance is compared with a traditional GA. Standard online and offline performance were evaluated and the obtained results were compared with those from a traditional GA. The basic idea of GAs has been taken from nature, therefore it is reasonable to expect an improvement in our new model for GA, because it is much closer to the evolution of organisms. Results provide valuable evidence that the proposed Adam-Eve-like GA outperforms the traditional GA in both online and offline performance measures. Based on the results from an optimization standpoint, the Adam-Eve-like GA seems to produce reasonable engineering results in an inherently difficult area where few other techniques are available. Furthermore they suggest that it may be beneficial to apply the algorithm on the real-world difficult optimization problems.
Starting Page	613
Ending Page	624
File Size	596795
Page Count	12
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.814162
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Stochastic processes Evolutionary computation Optimization methods Organisms Testing Systems engineering and theory Electronic switching systems Feedback Genetic mutations
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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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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