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      Levy walk evolution for global optimization

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      Author(s)
      Urfalıoğlu, Onay
      Çetin, A. Enis
      Kuruoğlu, E. E.
      Date
      2008-07
      Source Title
      GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
      Publisher
      ACM
      Pages
      537 - 538
      Language
      English
      Type
      Conference Paper
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      Abstract
      A novel evolutionary global optimization approach based on adaptive covariance estimation is proposed. The proposed method samples from a multivariate Levy Skew Alpha-Stable distribution with the estimated covariance matrix to realize a random walk and so to generate new solution candidates in the mutation step. The proposed method is compared to the popular Differential Evolution method, which is one of the best general evolutionary global optimizers available. Experimental results indicate that the proposed approach yields a general improvement in the required number of function evaluations to solve global optimization problems. Especially, as shown in experiments, the underlying heavy tailed alpha-stable distribution enables a considerably more effective global search in more complex problems. Track: Evolution Strategies.
      Keywords
      Alpha-stable distribution
      Evolutionary optimization
      Global optimization
      Heavy tailed distribution
      Levy walk
      Covariance matrix
      Evolutionary algorithms
      Function evaluation
      Optimization
      Alpha-stable distributions
      Complex problems
      Covariance estimations
      Differential evolution methods
      Evolution strategies
      Evolutionary global optimizations
      Global optimization problems
      Global searches
      New solutions
      Optimizers
      Random walks
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      http://hdl.handle.net/11693/26760
      Published Version (Please cite this version)
      https://doi.org/10.1145/1389095.1389200
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      • Department of Electrical and Electronics Engineering 3868
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