Solving Multi-objective Optimisation Problems Using the Potential Pareto Regions Evolutionary Algorithm

  • Nasreddine Hallam
  • Graham Kendall
  • Peter Blanchfield
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


In this paper we propose a novel multi-objective evolutionary algorithm that we call Potential Pareto Regions Evolutionary Algorithm (PPREA). Unlike state-of-the-art algorithms, which use a fitness assignment method based on Pareto ranking, the approach adopted in this work is new. The fitness of an individual is equal to the least improvement needed by that individual in order to reach non-dominance status.

This new algorithm is compared against the Nondominated Sorting Genetic Algorithm (NSGA-II) on a set of test suite problems derived from the works of researchers from MOEA community.


Multiobjective Optimisation Vector Solution Assignment Scheme Multiobjective Evolutionary Algorithm Nondominated Point 
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 2006

Authors and Affiliations

  • Nasreddine Hallam
    • 1
  • Graham Kendall
    • 1
  • Peter Blanchfield
    • 1
  1. 1.School of Computer Science and ITThe Univeristy of Nottingham 

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