Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-41278-3_67
Title: Machine learning enhanced multi-objective evolutionary algorithm based on decomposition
Authors: Liau, Y.S.
Tan, K.C. 
Hu, J.
Qiu, X.
Gee, S.B.
Keywords: Evolutionary multi-objective optimization
expensive optimization
support vector machine
surrogate modelling
Issue Date: 2013
Source: Liau, Y.S.,Tan, K.C.,Hu, J.,Qiu, X.,Gee, S.B. (2013). Machine learning enhanced multi-objective evolutionary algorithm based on decomposition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8206 LNCS : 553-560. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-41278-3_67
Abstract: We address the problem of expensive multi-objective optimization using a machine learning assisted model of evolutionary computation. Specifically, we formulate a meta-objective function tailored to the framework of MOEA/D, which can be solved by means of supervised regression learning using the Support Vector Machine (SVM) algorithm. The learned model constitutes the knowledge which can be then utilized to guide the evolution process within MOEA/D so as to reach better regions in the search space more quickly. Simulation results on a variety of benchmark problems show that the machine-learning enhanced MOEA/D is able to obtain better estimation of Pareto fronts when the allowed computational budget, measured in terms of number of objective function evaluation, is scarce. © 2013 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/70852
ISBN: 9783642412776
ISSN: 03029743
DOI: 10.1007/978-3-642-41278-3_67
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

4
checked on Dec 13, 2017

Page view(s)

60
checked on Dec 16, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.