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Univariate Marginal Distribution Algorithm in Combination with Extremal Optimization (EO, GEO)

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Neural Information Processing (ICONIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7063))

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Abstract

The UMDA algorithm is a type of Estimation of Distribution Algorithms. This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions. It can explore unknown parts of search space well. It uses a probability vector and individuals of the population are created through the sampling. Furthermore, EO algorithm is suitable for local search of near global best solution in search space, and it dose not stuck in local optimum. Hence, combining these two algorithms is able to create interaction between two fundamental concepts in evolutionary algorithms, exploration and exploitation, and achieve better results of this paper represent the performance of the proposed algorithm on two NP-hard problems, multi processor scheduling problem and graph bi-partitioning problem.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Islamic Azad University, Qazvin Branch, Qazvin, Iran

    Mitra Hashemi

  2. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

    Mohammad Reza Meybodi

Authors
  1. Mitra Hashemi
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  2. Mohammad Reza Meybodi
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Bao-Liang Lu  & Liqing Zhang  & 

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

    James Kwok

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© 2011 Springer-Verlag Berlin Heidelberg

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Hashemi, M., Meybodi, M.R. (2011). Univariate Marginal Distribution Algorithm in Combination with Extremal Optimization (EO, GEO). In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_26

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  • DOI: https://doi.org/10.1007/978-3-642-24958-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24957-0

  • Online ISBN: 978-3-642-24958-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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