Rotationally Invariant Crossover Operators in Evolutionary Multi-objective Optimization

  • Antony Iorio
  • Xiaodong Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


Multi-objective problems with parameter interactions can present difficulties to many optimization algorithms. We have investigated the behaviour of Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX) operator within the NSGA-II (Non-dominated Sorting Genetic Algorithm II) on four rotated test problems exhibiting parameter interactions. The rotationally invariant crossover operators demonstrated improved performance in optimizing the problems, over a non-rotationally invariant crossover operator.


Multiobjective Optimization Decision Space Traditional Genetic Algorithm Crossover Technique Simulated Binary Crossover 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Antony Iorio
    • 1
  • Xiaodong Li
    • 1
  1. 1.School of Computer Science and ITRMIT UniversityMelbourneAustralia

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