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Memetic Differential Evolution Frameworks in Filter Design for Defect Detection in Paper Production

  • Ferrante Neri
  • Ville Tirronen
Part of the Studies in Computational Intelligence book series (SCI, volume 213)

Abstract

This chapter studies and analyzes Memetic Differential Evolution (MDE) Frameworks for designing digital filters, which aim at detecting paper defects produced during an industrial process. MDE Frameworks employ the Differential Evolution (DE) as an evolutionary framework and a list of local searchers adaptively coordinated by a control scheme. Here, three different variants of MDE are taken into account and their features and performance are compared. The binomial explorative features of the DE framework in contraposition to the exploitative features of the local searcher are analyzed in detail in light of the stagnation prevention problem, typical for the DE. Much emphasis in this chapter is given to the various adaptation systems and to their applicability this image processing problem.

Keywords

Particle Swarm Optimization Differential Evolution Defect Detection Finite Impulse Response Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ferrante Neri
    • 1
  • Ville Tirronen
    • 1
  1. 1.Department of Mathematical Information Technology, AgoraUniversity of Jyväskylä(Agora)Finland

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