Study of Hierarchical Fuzzy Classification Systems by Adopting DNA Coding with Artificial Bee Colonies and Evolutionary Algorithm

Abstract

This dissertation proposes a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification The partition number and the shape of the membership function are extracted by the symbiosis based modified DNA- ABC optimization algorithm which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification According to literature the ABC algorithm is traditionally applied to constrained and unconstrained optimization problems but is combined with modified DNA concepts and implemented for fuzzy classification in this present research This dissertation also constructs the new variable coded hierarchical fuzzy models (VCHFM) synergistically integrates the standard fuzzy inference system and DNA coding with supervised learning to deal with the classification problems There are four stages to implement this model First a genetic algorithm (GA) procedure is used to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit Second the membership functions are adjusted by DNA computing Third the chaotic particle swarm optimization (CPSO) is used to regulate the weight grade of the principal output node of the inference unit Finally a multi-objective optimum fitness function is used to ensure the best classification rate with the minimum number and length of rules The proposed symbiosis based hybrid modified DNA-ABC optimization algorithm and VCHFM is validated through classifying the five benchmark databases: the UCI Pima Indians Diabetes Glass Wisconsin Breast Cancer Wine and Iris databases

Date of Award	2016 Jun 8
Original language	English
Supervisor	Tzuu-Hseng S. Li (Supervisor)