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Symbiotic evolution of rule based classifier systems

Halavati, R ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1142/S0218213009000019
  3. Publisher: 2009
  4. Abstract:
  5. Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a recombination operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design recombination operator, this paper presents a novel algorithm for rule base evolution based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. This operator takes two chromosomes with different number of genes (rules here) and merges them by combining all the information content of both chromosomes. Using this operator results in two major advantages: First, it totally removes the need to design the recombination operator and therefore is easier to use; second, it outperforms traditional genetic algorithm both in emergence speed and classification rate, this is tested and presented on some globally used benchmarks. © 2009 World Scientific Publishing Company
  6. Keywords:
  7. Classification rates ; Combination operators ; If-then rules ; Information contents ; Natural process ; Novel algorithms ; Recombination operators ; Rule base ; Rule based classifier ; Rule basis ; Sexual recombinations ; Symbiogenesis ; Symbiotic evolution ; Chromosomes ; Classifiers ; Fuzzy control ; Genetic algorithms ; Information management ; Learning systems ; Data mining
  8. Source: International Journal on Artificial Intelligence Tools ; Volume 18, Issue 1 , 2009 , Pages 1-16 ; 02182130 (ISSN)
  9. URL: https://www.worldscientific.com/doi/abs/10.1142/S0218213009000019