NDLI: An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-objective Evolutionary Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Zavoianu, A.-C. Lughofer, E. Bramerdorfer, G. Amrhein, W. Klement, E.P.
Copyright Year	2013
Description	Author affiliation: Inst. for Electr. Drives & Power Electron, Johannes Kepler Univ. of Linz, Linz, Austria (Bramerdorfer, G.; Amrhein, W.) \|\| Dept. of Knowledge-based Math. Syst. / Fuzzy Logic Lab. Linz-Hagenberg, Johannes Kepler Univ. of Linz, Linz, Austria (Zavoianu, A.-C.; Lughofer, E.; Klement, E.P.)
Abstract	The task of designing electrical drives is a multi-objective optimization problem (MOOP) that remains very slow even when using state-of-the-art approaches like particle swarm optimization and evolutionary algorithms because the fitness function used to assess the quality of a proposed design is based on time-intensive finite element (FE) simulations. One straightforward solution is to replace the original FE-based fitness function with a much faster-to-evaluate surrogate. In our particular case each optimization scenario poses rather unique challenges (i.e., goals and constraints) and the surrogate models need to be constructed on-the-fly, automatically, during the run of the evolutionary algorithm. In the present research, using three industrial MOOPs, we investigated several approaches for creating such surrogate models and discovered that a strategy that uses ensembles of multi-layer perceptron neural networks and Pareto-trimmed training sets is able to produce very high quality surrogate models in a relatively short time interval.
Starting Page	235
Ending Page	242
File Size	753511
Page Count	8
File Format	PDF
ISBN	9781479930357
e-ISBN	9781479930364
DOI	10.1109/SYNASC.2013.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-23
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training artificial neural networks Accuracy Computational modeling multi-objective evolutionary algorithms ensemble regression models Evolutionary computation Predictive models Data models surrogate fitness evaluation Optimization
Content Type	Text
Resource Type	Article

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