Particle Swarm Optimization Based Tuning of Genetic Programming Evolved Classifier Expressions

• Hajira Jabeen ^[1] ; Abdul Rauf Baig ^[1]
  1. [1] National University of Computer and Emerging Sciences

     National University of Computer and Emerging Sciences

     Pakistán

     
• Localización: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) / coord. por Juan R. González, David Alejandro Pelta Mochcovsky, Carlos Cruz, Germán Terrazas, Natalio Krasnogor, 2010, ISBN 978-3-642-12537-9, págs. 385-397
• Idioma: inglés
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• Resumen

  • Genetic Programming (GP) has recently emerged as an effective technique for classifier evolution. One specific type of GP classifiers is arithmetic classifier expression trees. In this paper we propose a novel method of tuning these arithmetic classifiers using Particle Swarm Optimization (PSO) technique. A set of weights are introduced into the bottom layer of evolved GP classifier expression tree, associated with each terminal node. These weights are initialized with random values and optimized using PSO. The proposed tuning method is found efficient in increasing performance of GP classifiers with lesser computational cost as compared to GP evolution for longer number of generations. Wehave conducted a series of experiments over datasets taken from UCI ML repository. Our proposed technique has been found successful in increasing the accuracy of classifiers in much lesser number of function evaluations.