NDLI: Improvements in genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Vasconcelos, J.A. Ramirez, J.A. Takahashi, R.H.C. Saldanha, R.R.
Copyright Year	1965
Abstract	This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several operators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic change of its values during the optimization process. In addition, the space reduction of the design variables and global elitism are analyzed. All GAs are effective when used with its best operations and values of parameters. For each GA, both sets of best operation types and parameters are found. The dynamic change of crossover and mutation probabilities, the space reduction and the global elitism during the evolution process show that great improvement can be achieved for all GA types. These GAs are applied to TEAM benchmark problem 22.
Sponsorship	IEEE Magnetics Society
Starting Page	3414
Ending Page	3417
Page Count	4
File Size	111845
File Format	PDF
ISSN	00189464
Volume Number	37
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Genetic mutations Performance analysis Optimization methods Steady-state Helium Performance evaluation Benchmark testing Convergence Biological cells
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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

Efficient genetic algorithms for solving hard constrained optimization problems

Genetic algorithms with assistant chromosomes for inverse shape optimization of electromagnetic devices

An improved genetic algorithm for global optimization of electromagnetic problems

Genetic multiway partitioning

Optimal transposition design of transformer windings by Genetic Algorithms

Multiobjective genetic algorithms applied to solve optimization problems

Stochastic algorithms in electromagnetic optimization

Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals

A study on comparison of optimization performances between immune algorithm and other heuristic algorithms

Improvements in genetic algorithms

Similar Documents

Efficient genetic algorithms for solving hard constrained optimization problems

Genetic algorithms with assistant chromosomes for inverse shape optimization of electromagnetic devices

An improved genetic algorithm for global optimization of electromagnetic problems

Genetic multiway partitioning

Optimal transposition design of transformer windings by Genetic Algorithms

Multiobjective genetic algorithms applied to solve optimization problems

Stochastic algorithms in electromagnetic optimization

Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals

A study on comparison of optimization performances between immune algorithm and other heuristic algorithms

Improvements in genetic algorithms