Meta-heuristic optimization of a fuzzy character recognizer

Alex Tormási, László T. Kóczy

Research output: Contribution to journalArticle

Abstract

Meta-heuristic algorithms are well researched and widely used in optimization problems. There are several meta-heuristic optimization algorithms with various concepts and each has its own advantages and disadvantages. Still it is difficult to decide which method would fit the best to a given problem. In this study the optimization of a fuzzy rule-base from a classifier, more specifically fuzzy character recognizer is used as the reference problem and the aim of the research was to investigate the behavior of selected meta-heuristic optimization techniques in order to develop a multi meta-heuristic algorithm.

Original languageEnglish
Pages (from-to)227-244
Number of pages18
JournalStudies in Fuzziness and Soft Computing
Volume326
DOIs
Publication statusPublished - Jan 1 2015

Keywords

  • Bacterial evolutionary algorithm Big bang–big crunch algorithm
  • Fuzzy rule-base optimization
  • Fuzzy systems
  • Imperialist competitive algorithm
  • Multi meta-heuristics
  • Particle swarm optimization

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics

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