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Preventing premature convergence and proving the optimality in evolutionary algorithms

Abstract : 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 inability to quickly compute a good approximation of the global minimum and their exponential complexity. 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 outperforms 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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Contributor : Charlie Vanaret <>
Submitted on : Thursday, November 7, 2013 - 1:22:37 AM
Last modification on : Thursday, March 26, 2020 - 6:41:45 PM
Document(s) archivé(s) le : Saturday, February 8, 2014 - 4:35:28 AM


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  • HAL Id : hal-00880716, version 1


Charlie Vanaret, Jean-Baptiste Gotteland, Nicolas Durand, Jean-Marc Alliot. Preventing premature convergence and proving the optimality in evolutionary algorithms. EA 2013, 11th International Conference on Artificial Evolution, Oct 2013, Bordeaux, France. pp 84-94 ; ISBN : 9782953926736. ⟨hal-00880716⟩



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