Michigan vs. Pittsburgh Style GA Optimisation of Fuzzy Rule Bases for Gene Expression Analysis
Gerald Schaefer, Tomoharu Nakashima · International Journal of Fuzzy System Applications · 2013
Microarray studies and gene expression analysis have received a lot of attention and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper, the authors perform gene expression analysis and apply two hybrid GA-fuzzy approaches to classify gene expression data. Both are based on fuzzy if-then rule bases but they differ in the way these rule bases are optimised. The authors employ both a Michigan style approach, where single rules are handled as individuals in the population of the genetic algorithm, and a Pittsburgh type algorithm, which treats whole rule sets as individuals. Experimental results show that both approaches achieve good classification accuracy but that the Michigan style algorithm clearly outperforms the Pittsburgh classifier.