Journal ArticleDOI
A Framework for Computational Design Synthesis: Model and Applications
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TLDR
A model of the automated synthesis process is provided as a context to discuss research in the area and some guidelines are presented to help researchers and designers find approaches to solving their particular design problems using computational design synthesis.Abstract:
The field of computational design synthesis has been an active area of research for almost half a century. Research advances in this field have increased the sophistication and complexity of the designs that can be synthesized, and advances in the speed and power of computers have increased the efficiency with which those designs can be generated. Some of the results of this research have begun to be used in industrial practice, yet many open issues and research challenges remain. This paper provides a model of the automated synthesis process as a context to discuss research in the area. The varied works of the authors are discussed as representative of the breadth of methods and results that exist under the field of computational design synthesis. Furthermore, some guidelines are presented to help researchers and designers find approaches to solving their particular design problems using computational design synthesis. DOI: 10.1115/1.2013289read more
Citations
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Machine learning
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Engineering Design Processes: A Comparison of Students and Expert Practitioners
Cynthia J. Atman,Robin Adams,Monica E. Cardella,Jennifer Turns,Susan Mosborg,Jason J. Saleem +5 more
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A review of function modeling: Approaches and applications
Mustafa Suphi Erden,H. Komoto,T. J. van Beek,Valentina D'Amelio,E Echavarría,Tetsuo Tomiyama +5 more
TL;DR: The goals are to highlight the features of various classical approaches in relation to FM, to delineate what FM introduces to these fields, and to discuss the applicability of various FM approaches in these fields.
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Computational design in architecture: Defining parametric, generative, and algorithmic design
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Tools and techniques for product design
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References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.