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Interleaving Innovization with Evolutionary Multi-Objective Optimization in Production System Simulation for Faster Convergence Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Automated Parameter Tuning Framework for Heterogeneous and Large Instances: Case Study in Quadratic Assignment Problem

Authors : Lindawati, Zhi Yuan, Hoong Chuin Lau, Feida Zhu

Published in: Learning and Intelligent Optimization

Publisher: Springer Berlin Heidelberg

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Abstract

This paper is concerned with automated tuning of parameters of algorithms to handle heterogeneous and large instances. We propose an automated parameter tuning framework with the capability to provide instance-specific parameter configurations. We report preliminary results on the Quadratic Assignment Problem (QAP) and show that our framework provides a significant improvement on solutions qualities with much smaller tuning computational time.

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previous chapter Tuning Algorithms for Tackling Large Instances: An Experimental Protocol
next chapter Practically Desirable Solutions Search on Multi-Objective Optimization
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Metadata
Title
Automated Parameter Tuning Framework for Heterogeneous and Large Instances: Case Study in Quadratic Assignment Problem
Authors
Lindawati
Zhi Yuan
Hoong Chuin Lau
Feida Zhu
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-642-44972-7

Electronic ISBN: 978-3-642-44973-4

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-44973-4_45

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