Bi-objective Portfolio Optimization Using a Customized Hybrid NSGA-II Procedure

  • Kalyanmoy Deb
  • Ralph E. Steuer
  • Rajat Tewari
  • Rahul Tewari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6576)


Bi-objective portfolio optimization for minimizing risk and maximizing expected return has received considerable attention using evolutionary algorithms. Although the problem is a quadratic programming (QP) problem, the practicalities of investment often make the decision variables discontinuous and introduce other complexities. In such circumstances, usual QP solution methodologies can not always find acceptable solutions. In this paper, we modify a bi-objective evolutionary algorithm (NSGA-II) to develop a customized hybrid NSGA-II procedure for handling situations that are non-conventional for classical QP approaches. By considering large-scale problems, we demonstrate how evolutionary algorithms enable the proposed procedure to find fronts, or portions of fronts, that can be difficult for other methods to obtain.


Local Search Quadratic Program Mutation Operator Portfolio Optimization Portfolio Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kalyanmoy Deb
    • 1
    • 2
  • Ralph E. Steuer
    • 3
  • Rajat Tewari
    • 4
  • Rahul Tewari
    • 5
  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia
  2. 2.Department of Business TechnologyAalto University, School of EconomicsHelsinkiFinland
  3. 3.Department Banking and Finance, Terry College of BusinessUniversity of GeorgiaAthensUSA
  4. 4.Benaras Hindu UniversityBenarasIndia
  5. 5.Deutsche Bank GroupMumbaiIndia

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