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      • Department of Electrical and Electronics Engineering
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      Randomized and rank based differential evolution

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      Author(s)
      Urfalıoğlu, Onay
      Arıkan, Orhan
      Date
      2009-12
      Source Title
      8th International Conference on Machine Learning and Applications, ICMLA 2009
      Publisher
      IEEE
      Pages
      95 - 100
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Many real world problems which can be assigned to the machine learning domain are inverse problems. The available data is often noisy and may contain outliers, which requires the application of global optimization. Evolutionary Algorithms (EA's) are one class of possible global optimization methods for solving such problems. Within population based EA's, Differential Evolution (DE) is a widely used and successful algorithm. However, due to its differential update nature, given a current population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitnesses of the population. This paper presents a novel extension of DE called Randomized and Rank based Differential Evolution (R2DE) to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the fitnesses. In experiments including non-linear dimension reduction by autoencoders, it is shown that R2DE improves robustness and speed of global convergence. © 2009 IEEE.
      Keywords
      Autoencoders
      Cauchy distribution
      Differential Evolution
      Global convergence
      Global optimization method
      Machine-learning
      Multimodal problems
      Non-linear
      Random variates
      Real-world problem
      Evolutionary algorithms
      Global optimization
      Inverse problems
      Learning systems
      Problem solving
      Permalink
      http://hdl.handle.net/11693/28647
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ICMLA.2009.29
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      • Department of Electrical and Electronics Engineering 3702
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