Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parametric and Feature Based CAD/Cam: Concepts, Techniques, and Applications
Parametric and Feature Based CAD/Cam: Concepts, Techniques, and Applications
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multi-objective optimization in architectural design
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Real-time design feedback: coupling performance-knowledge with design iteration for decision-making
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Advanced Engineering Informatics
Automated energy model creation for conceptual design
Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design
An integrated approach to algorithmic design and environmental analysis
Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design
Multicriteria optimization of paneled building envelopes using ant colony optimization
EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
Multi-level interaction in parametric design
SG'05 Proceedings of the 5th international conference on Smart Graphics
Proceedings of the Symposium on Simulation for Architecture & Urban Design
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The overall performance of buildings is heavily impacted by design decisions made during the early stages of the design process. Design professionals are most often unable to explore design alternatives and their impact on energy profiles adequately during this phase. Combining parametric modeling with multi-disciplinary design optimization has been previously identified as a potential solution. By utilizing parametric design and multi-disciplinary design optimization to influence design at the schematic level in the interest of exploring more energy efficient design configurations, the H. D. S. Beagle 1.0 tool was developed. The tool enables the generation of design alternatives according to user defined parameter ranges; automatically gathers the energy analysis result of each design alternative; automatically calculates three objective functions; and uses Genetic Algorithm to intelligently search, rank, select, and breed the solution space for decision making. Current case studies demonstrate our tool's ability to reduce design cycle latency and improve quality. However, the future work is needed to further investigate how to acclimate this process to accommodate early design stages and processes.