Scientific American
Mathematical elements for computer graphics (2nd ed.)
Mathematical elements for computer graphics (2nd ed.)
Graphical representation of design optimization processes
Computer-Aided Design
First leaves: a tutorial introduction to Maple V
First leaves: a tutorial introduction to Maple V
Retrieval strategies in a case-based design system
Artificial intelligence in engineering design (Vol. II)
Machine learning in engineering: techniques to speed up numerical optimization
Machine learning in engineering: techniques to speed up numerical optimization
C4.5: programs for machine learning
C4.5: programs for machine learning
Case-based reasoning
Activity analysis: the qualitative analysis of stationary points for optimal reasoning
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Decision Support Systems - Special double issue: unified programming
Learning Logical Definitions from Relations
Machine Learning
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
The use of artificial intelligence to improve the numerical optimization of complex engineering designs
Principles of Optimal Design
Artificial intelligence for design
Formal engineering design synthesis
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Using modeling knowledge to guide design space search
Artificial Intelligence
Learning symbolic formulations in design: Syntax, semantics, and knowledge reification
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
CBE-Conveyor: a case-based reasoning system to assist engineers in designing conveyor systems
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the objective function and constraint functions are continuous, smooth, and defined everywhere. Unfortunately, realistic simulators tend to violate these assumptions, making optimization unreliable. Several decisions that need to be made in setting up an optimization, such as the choice of a starting prototype and the choice of a formulation of the search space, can make a difference in the reliability of the optimization. Machine learning can improve gradient-based methods by making these choices based on the results of previous optimizations. This paper demonstrates this idea by using machine learning for four parts of the optimization setup problem: selecting a starting prototype from a database of prototypes, synthesizing a new starting prototype, predicting which design goals are achievable, and selecting a formulation of the search space. We use standard tree-induction algorithms (C4.5 and CART). We present results in two realistic engineering domains: racing yachts and supersonic aircraft. Our experimental results show that using inductive learning to make setup decisions improves both the speed and the reliability of design optimization.