@inproceedings{88e39834b7c341039c1d4e7fba8eda5a,
title = "Machine learning enhanced multi-objective evolutionary algorithm based on decomposition",
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. ",
keywords = "Evolutionary multi-objective optimization, expensive optimization, support vector machine, surrogate modelling",
author = "Liau, \{Yung Siang\} and Tan, \{Kay Chen\} and Jun Hu and Xin Qiu and Gee, \{Sen Bong\}",
year = "2013",
month = oct,
doi = "10.1007/978-3-642-41278-3\_67",
language = "English",
isbn = "978-3-642-41277-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Berlin Heidelberg",
pages = "553--560",
editor = "Hujun Yin and Ke Tang and Yang Gao and Klawonn, \{Frank \} and Minho Lee and Weise, \{Thomas \} and Bin Li and Xin Yao",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2013",
note = "14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013 ; Conference date: 20-10-2013 Through 23-10-2013",
}