The visual display of quantitative information
The visual display of quantitative information
Algorithms for clustering data
Algorithms for clustering data
Envisioning information
Design problem solving: a task analysis
AI Magazine
Exploration and innovation in design: towards a computational model
Exploration and innovation in design: towards a computational model
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Spatial aggregation: theory and applications
Journal of Artificial Intelligence Research
Multicriterially Best Explanations
DS '01 Proceedings of the 4th International Conference on Discovery Science
Accelerating design space exploration using pareto-front arithmetics
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Towards an agent-based framework for guiding design exploration
Proceedings of the 2008 international workshop on Recommendation systems for software engineering
Solving hierarchical optimization problems using MOEAs
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Advanced Engineering Informatics
Initial population construction for convergence improvement of MOEAs
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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We describe an architecture for exploring very large design spaces, for example, spaces that arise when design candidates are generated by combining components systematically from component libraries. A very large number of candidates are methodically considered and evaluated. This architecture is especially appropriate during the stage of conceptual design when high-level design decisions are under consideration, multiple evaluation criteria apply, and a designer seeks assurance that good design possibilities have not been overlooked. We present a filtering technique based on a dominance criterion that can be used to select, from millions of design candidates, a relatively small number of promising candidates for further analysis. The dominance criterion is lossless in that it insures that each candidate not selected is inferior to at least one of the selected candidates. We also describe an interactive interface in which the selected designs are presented to the designer for analysis of tradeoffs and further exploration. In our current implementation, the computational load is distributed among a large number of workstations in a client-server computing environment. We describe the results of experiments using the architecture to explore designs for hybrid electric vehicles. In a recent experiment more than two million distinct designs were evaluated.