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Automatic estimation of differential evolution parameters using Hidden Markov Models

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Abstract

Differential evolution (DE) has been successful in solving practical optimization problem. However, similar to other optimization algorithms, the search performance of DE depends on the efficacy of the adopted search operators. The ability to adapt these operators within an evolutionary run enhances their ability to find better quality solutions. This adaptation process requires learning algorithms capable of compressing the information embedded within a population into meaningful estimates to adapt the search operators. Hidden Markov Models (HMMs) are learning algorithms designed to estimate parameters by compressing information collected from on a state space. In this paper, we use HMMs to compress the information within a population and use the model for adapting the DE parameters. The resultant DE-HMM algorithm dynamically adjusts the two basic parameters of DE. After a thorough testing of this method and conducting an extensive comparison of its performance on the CEC2005 and CEC2014 dataset, it is shown that the proposed DE-HMM algorithm is able to achieve better results compared with the classical DE and other state-of-the-art methods. On average, the algorithm can achieve this performance faster than other methods in the literature.

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Authors and Affiliations

  1. University of New South Wales-Canberra, Canberra, Australia

    Marwa Keshk, Hemant Singh & Hussein Abbass

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  1. Marwa Keshk
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  2. Hemant Singh
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  3. Hussein Abbass
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Correspondence to Marwa Keshk.

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Keshk, M., Singh, H. & Abbass, H. Automatic estimation of differential evolution parameters using Hidden Markov Models. Evol. Intel. 10, 77–93 (2018). https://doi.org/10.1007/s12065-018-0153-5

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