A survey on automating configuration and parameterization in evolutionary design exploration

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 optimi...

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Bibliographic Details
Published in:AI EDAM 2015-11, Vol.29 (4), p.333-350
Main Authors: Eichhoff, Julian R., Roller, Dieter
Format: Article
Language:English
Subjects:
Artificial intelligence
Design analysis
Design engineering
Design optimization
Exploration
Extraction
Mathematical analysis
Mathematical models
Natural language processing
Parametrization
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Summary: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.
ISSN:0890-0604
1469-1760
DOI:10.1017/S0890060415000372