Evolutionary Learning for Soft Margin Problems: A Case Study on Practical Problems with Kernels

Wenjun Wang, Wei Pang, Paul A. Bingham, Mania Mania, Tzu-Yu Chen, Justin J. Perry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
35 Downloads (Pure)


This paper addresses two practical problems: the classification and prediction of properties for polymer and glass materials, as a case study of evolutionary learning for tackling soft margin problems. The presented classifier is modelled by support vectors as well as various kernel functions, with its hard restrictions relaxed by slack variables to be soft restrictions in order to achieve higher performance. We have compared evolutionary learning with traditional gradient methods on standard, dual and soft margin support vector machines, built by polynomial, Gaussian, and ANOVA kernels. Experimental results for data on 434 polymers and 1,441 glasses show that both gradient and evolutionary learning approaches have their advantages. We show that within this domain the chosen gradient methodology is beneficial for standard linear classification problems, whilst the evolutionary methodology is more effective in addressing highly non-linear and complex problems, such as the soft margin problem.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
ISBN (Electronic)9781728169293
Publication statusPublished - 3 Sept 2020


  • evolutionary learning
  • kernel function
  • slack variables
  • soft margin
  • support vector

ASJC Scopus subject areas

  • Control and Optimization
  • Decision Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture


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