Inverse multi-objective robust evolutionary design
Lim, D, Ong, Y-S, Jin, Y, Sendhoff, B and Lee, BS (2006) Inverse multi-objective robust evolutionary design Genetic Programming and Evolvable Machines, 7 (4). pp. 383-404.
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In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Identification Number :||https://doi.org/10.1007/s10710-006-9013-7|
|Additional Information :||Copyright Springer Science + Business Media, LLC 2006.The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||12 Mar 2012 11:40|
|Last Modified :||23 Sep 2013 18:49|
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