hypDE: A Hyper-Heuristic Based on Differential Evolution for Solving Constrained Optimization Problems

  • José Carlos Villela Tinoco
  • Carlos A. Coello Coello
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)


In this paper, we present a hyper-heuristic, based on Differential Evolution, for solving constrained optimization problems. Differential Evolution has been found to be a very effective and efficient optimization algorithm for continuous search spaces, which motivated us to adopt it as our search engine for dealing with constrained optimization problems. In our proposed hyper-heuristic, we adopt twelve differential evolution models for our low-level heuristic.We also adopt four selection mechanisms for choosing the low-level heuristic. The proposed approach is validated using a well-known benchmark for constrained evolutionary optimization. Results are compared with respect to those obtained by a state-of-theart constrained differential evolution algorithm (CDE) and another hyper-heuristic that adopts a random descent selection mechanism. Our results indicate that our proposed approach is a viable alternative for dealing with constrained optimization problems.


Test Problem Differential Evolution Selection Mechanism Constrain Optimization Problem Differential Evolution Algorithm 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Carlos Villela Tinoco
    • 1
  • Carlos A. Coello Coello
    • 1
  1. 1.Departamento de ComputaciónCINVESTAV-IPN (Evolutionary Computation Group)MéxicoMéxico

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