Jumping genes in evolutionary computing

Abstract

Jumping Genes Genetic Algorithm (JGGA) is a newly developed algorithm that is suitable for evolutionary computing. This algorithm is characterized by a genetic operator other than crossover and mutation, which is Jumping Genes Transposition. Jumping Genes Transposition can increase the ability of Genetic Algorithm in finding extreme solutions, and increase the diversity of the solutions at the same time. In this project, the performances of JGGA will be compared to the performances of Nondominated Sorting Genetic Algorithm II (NSGA-II) in order to evaluate the performance of Jumping Genes Transposition. JGGA and NSGA-II is very similar, and their difference is only the existence of Jumping Genes Transposition in JGGA. The designs of HGA IIR filters and digital filter banks are implemented using JGGA and NSGA-II, and these GA programs are used for the performance evaluation of the two algorithms.