NDLI: A novel hybrid genetic algorithm for constrained optimization

Content Provider	Springer Nature Link
Author	Deep, Kusum Das, Kedar Nath
Copyright Year	2013
Abstract	Finding global optimal solution of a non-linear constrained optimization problem with high complexity is a challenge for the researchers. Now days, real coded genetic algorithm (GA) becomes popular to solve them, due to their diversity preserving mechanism. In recent literature it is proved that for solving constrained optimization problem, the real coded GA (LX-PM) that uses Laplace Crossover and Power Mutation, is much efficient. In this paper an attempt is made to improve the performance of LX-PM, hybridizing with Quadratic Approximation. The efficiency and reliability of the design hybrid algorithm is realized through a set of 15 benchmark test problems.
Starting Page	86
Ending Page	93
Page Count	8
File Format	PDF
ISSN	09756809
Journal	International Journal of System Assurance Engineering and Management
Volume Number	4
Issue Number	1
e-ISSN	09764348
Language	English
Publisher	Springer-Verlag
Publisher Date	2013-01-22
Publisher Place	India
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Real-coded genetic algorithms Hybrid genetic algorithms Quadratic approximation Constrained optimization Penalty function Quality Control, Reliability, Safety and Risk Engineering Economics, Organization, Logistics, Marketing
Content Type	Text
Resource Type	Article
Subject	Strategy and Management Safety, Risk, Reliability and Quality

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A novel hybrid genetic algorithm for constrained optimization