Preventing Premature Convergence and Proving the Optimality in Evolutionary Algorithms

  • Charlie Vanaret
  • Jean-Baptiste Gotteland
  • Nicolas Durand
  • Jean-Marc Alliot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8752)


Evolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their exponential complexity and their inability to quickly compute a good approximation of the global minimum. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a branch and bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and is highly competitive with both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Charlie Vanaret
    • 1
    • 2
  • Jean-Baptiste Gotteland
    • 1
    • 2
  • Nicolas Durand
    • 1
    • 2
  • Jean-Marc Alliot
    • 2
  1. 1.Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l’AérienEcole Nationale de l’Aviation CivileToulouseFrance
  2. 2.Institut de Recherche en Informatique de ToulouseToulouseFrance

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