NDLI: Greedy Population Sizing for Evolutionary Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Smorodkina, E. Tauritz, D.
Copyright Year	2007
Description	Author affiliation: Univ. of Missouri-Rolla, Rolla (Smorodkina, E.; Tauritz, D.)
Abstract	The number of parameters that need to be manually tuned to achieve good performance of evolutionary algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a greedy population sizing method for evolutionary algorithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS-EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually.
Starting Page	2181
Ending Page	2187
File Size	131828
Page Count	7
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Genetic algorithms Robustness Convergence
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

Parental population sizing in evolutionary strategies

Visualization of evolutionary computation processes: from the perspective of population

Computable rate of convergence in evolutionary computation

Population learning algorithm versus evolutionary computation

Rate of convergence in evolutionary computation

Range juggling for better convergence in genetic range genetic algorithms

A genetic algorithm for lot sizing optimization with a capacity loading criterion

Population Sizing Scheme for Genetic Algorithm

Enhancing Population Diversity for Genetic Algorithms

Greedy Population Sizing for Evolutionary Algorithms

Similar Documents

Parental population sizing in evolutionary strategies

Visualization of evolutionary computation processes: from the perspective of population

Computable rate of convergence in evolutionary computation

Population learning algorithm versus evolutionary computation

Rate of convergence in evolutionary computation

Range juggling for better convergence in genetic range genetic algorithms

A genetic algorithm for lot sizing optimization with a capacity loading criterion

Population Sizing Scheme for Genetic Algorithm

Enhancing Population Diversity for Genetic Algorithms

Greedy Population Sizing for Evolutionary Algorithms