Evolution’s Niche in Multi-Criterion Problem Solving

  • Kalyanmoy Deb
Part of the Studies in Computational Intelligence book series (SCI, volume 210)


In a short span of about 15 years, evolutionary multi-objective optimization (EMO) has progressed on a fast track in proposing, implementing, and applying efficient methodologies based on nature-inspired computational algorithms for optimization. In this chapter, we briefly describe the original motivation for developing EMO algorithms and provide an account of some successful problem domains on which EMO has demonstrated a clear edge over their classical counterparts. More success studies exist and many more problem areas are needed to be explored. Hopefully, this chapter provides an indication and flavor of some such problem domains which may get benefited from a systematic application of an EMO procedure.


Multiobjective Optimization Reference Point Method Reservation Point Light Beam Search Scalarized Generate Method 
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 2009

Authors and Affiliations

  • Kalyanmoy Deb
    • 1
    • 2
  1. 1.Department of Business TechnologyHelsinki School of EconomicsHelsinkiFinland
  2. 2.Department of Mechanical EngineeringIndian Institute of Technology KanpurIndia

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