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Author Shoaf, J. ♦ Foster, J.A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1998
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Portfolios ♦ Security ♦ Resource management ♦ Equations ♦ Quadratic programming ♦ Investments ♦ Covariance matrix ♦ Wheels ♦ Computer science ♦ Genetic algorithms
Abstract The genetic algorithm (GA) for the efficient set portfolio problem based on the Markowitz model, introduced by Shoaf and Foster (1996), offers significant benefits over the quadratic programming approach. These benefits include simultaneous optimization of risk and return. The efficient set GA uses an indirect representation style in order to avoid infeasible solutions and penalty functions. The success of this approach had raised questions about the scalability of this GA. New empirical results confirm that the efficient set GA scales well with time complexity O(n log n) for portfolios containing up to n=100 stocks. Additional experiments also show that a deme implementation extends the period of active solution improvement for this GA.
Description Author affiliation: Dept. of Comput. Sci., Idaho Univ., Moscow, ID, USA (Shoaf, J.)
ISBN 0780348699
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-05-04
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 564.50 kB
Page Count 6
Starting Page 354
Ending Page 359


Source: IEEE Xplore Digital Library