University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Data-Driven Surrogate-Assisted Multi-Objective Evolutionary Optimization of A Trauma System

Jin, Y, Wang, H and Jansen, JO (2016) Data-Driven Surrogate-Assisted Multi-Objective Evolutionary Optimization of A Trauma System IEEE Transactions on Evolutionary Computation.

[img]
Preview
Text
GEOS_Final.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (5MB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

Most existing work on evolutionary optimization assumes that there are analytic functions for evaluating the objectives and constraints. In the real-world, however, the objective or constraint values of many optimization problems can be evaluated solely based on data and solving such optimization problems is often known as data-driven optimization. In this paper, we divide data-driven optimization problems into two categories, i.e., off-line and on-line data-driven optimization, and discuss the main challenges involved therein. An evolutionary algorithm is then presented to optimize the design of a trauma system, which is a typical off-line data-driven multi-objective optimization problem, where the objectives and constraints can be evaluated using incidents only. As each single function evaluation involves large amount of patient data, we develop a multi-fidelity surrogate management strategy to reduce the computation time of the evolutionary optimization. The main idea is to adaptively tune the approximation fidelity by clustering the original data into different numbers of clusters and a regression model is constructed to estimate the required minimum fidelity. Experimental results show that the proposed algorithm is able to save up to 90% of computation time without much sacrifice of the solution quality.

Item Type: Article
Subjects : subj_Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Jin, YUNSPECIFIEDUNSPECIFIED
Wang, HUNSPECIFIEDUNSPECIFIED
Jansen, JOUNSPECIFIEDUNSPECIFIED
Date : 20 April 2016
Identification Number : 10.1109/TEVC.2016.2555315
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : data-driven optimization, multi-objective optimization, evolutionary algorithm, surrogate, trauma system design
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 06 May 2016 15:39
Last Modified : 05 Jul 2016 13:22
URI: http://epubs.surrey.ac.uk/id/eprint/810649

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800