Hybrid evolutionary particle swarm optimization and ant colony optimization for variable selection

Carlos Cernuda, Edwin Lughofer, Wolfgang Märzinger, Wolfram Summerer

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Nowadays the techniques employed in data acquisition in Chemometrics (e.g. NIR or MIR) can provide huge amounts of data in a cheap way. Thus a tsunami of data, where the number of variables explodes, must be employed, being necessary a variable selection approach as a previous step in any classification or regression problem. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are searching algorithms that have been recently used for that purpose. Due to the nature of the search procedures, both suffer from the problem of being trapped in local optima that could differ much from the global ones, which are unknown. In the line of Differential Evolution, the authors propose a hybridization of both algorithms by means of a Genetic Algorithm (GA) approach, which combines the advantages of both searching algorithms and promotes escaping from local optima
Original languageEnglish
Title of host publicationProceedings of the 3rd World Conference on Information Technology (WCIT-2012)
PublisherAWERProcedia Information Technology & Computer Science,
Pages7-14
Number of pages8
Volume3
Publication statusPublished - 2013

Publication series

Name3rd World Conference on Information Technology (WCIT-2012)

Fields of science

  • 101001 Algebra
  • 101 Mathematics
  • 102 Computer Sciences
  • 101013 Mathematical logic
  • 101020 Technical mathematics
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 202027 Mechatronics
  • 101019 Stochastics
  • 211913 Quality assurance

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Mechatronics and Information Processing
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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