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Network-Based Problem Difficulty Prediction Measures

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Evolutionary Computation and Complex Networks

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Abstract

The most important purpose of problem difficulty analysis is to design measures that can easily evaluate the difficulty of different types of problems for EAs. In this chapter, existing predictive problem difficulty measures are introduced; first, nonnetwork-based measures are introduced briefly, and then the network-based measures are introduced in detail, with thorough experiments to illustrate their performance.

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Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, Shaanxi, China

    Jing Liu

  2. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

    Hussein A. Abbass

  3. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong SAR

    Kay Chen Tan

Authors
  1. Jing Liu
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  2. Hussein A. Abbass
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  3. Kay Chen Tan
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Corresponding author

Correspondence to Jing Liu .

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Liu, J., Abbass, H.A., Tan, K.C. (2019). Network-Based Problem Difficulty Prediction Measures. In: Evolutionary Computation and Complex Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-60000-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-60000-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59998-4

  • Online ISBN: 978-3-319-60000-0

  • eBook Packages: EngineeringEngineering (R0)

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