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A survey on automating configuration and parameterization in evolutionary design exploration

Published online by Cambridge University Press:  07 October 2015

Julian R. Eichhoff  and
Dieter Roller
Julian R. Eichhoff*
Affiliation:
Institute of Computer-Aided Product Development Systems, University of Stuttgart, Stuttgart, Germany
Dieter Roller
Affiliation:
Institute of Computer-Aided Product Development Systems, University of Stuttgart, Stuttgart, Germany
*
Reprint requests to: Julian Eichhoff, Institut für Rechnergestützte Ingenieursysteme, Universität Stuttgart, Universitätsstrasse 38, Stuttgart D-70569, Germany. E-mail: julian.eichhoff@informatik.uni-stuttgart.de
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Abstract

Configuration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

Keywords

Conceptual DesignDesign AutomationDesign OptimizationEvolutionary ComputingKnowledge EngineeringMachine Learning
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 333 - 350
Copyright
Copyright © Cambridge University Press 2015 

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