An improved Non-dominated Sorting Genetic Algorithm-II (ANSGA-II) with adaptable parameters Online publication date: Thu, 03-Sep-2009
by Khoa Duc Tran
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 7, No. 4, 2009
Abstract: Multi-Objective Evolutionary Algorithms (MOEAs) are not easy to use because they require parameter tunings to achieve good solutions and performance for an arbitrary complex problem. This paper introduces a MOEA with adaptive population size, self-adaptive crossover and self-adaptive mutation for automating the process of adjusting parameter values to make the MOEA simple to use. The new MOEA is built on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and named as Adaptable NSGA-II (ANSGA-II). Simulation results on 13 multi-objective problems demonstrate that the ANSGA-II out-performs the NSGA-II in terms of finding diverse non-dominated solutions and converging to the true Pareto-optimal front.
Online publication date: Thu, 03-Sep-2009
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com