Structured design of microelectromechanical systems
DAC '97 Proceedings of the 34th annual Design Automation Conference
3D game engine design: a practical approach to real-time computer graphics
3D game engine design: a practical approach to real-time computer graphics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using assembly representations to enable evolutionary design of Lego structures
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Introduction to the Practice of Statistics Minitab Manual and Minitab Version 14
Introduction to the Practice of Statistics Minitab Manual and Minitab Version 14
Assembly synthesis with subassembly partitioning for optimal in-process dimensional adjustability
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Simultaneous multi-topology multi-objective sizing across thousands of analog circuit topologies
Proceedings of the 44th annual Design Automation Conference
Structured synthesis of MEMS using evolutionary approaches
Applied Soft Computing
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Evolving MEMS Resonator Designs for Fabrication
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
System-level synthesis of MEMS via genetic programming and bond graphs
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Original Articles: Interactive hybrid evolutionary computation for MEMS design synthesis
Mathematics and Computers in Simulation
| Hi-index | 0.00 |
In this paper we present a genotype representation method for improving the performance of genetic-algorithm-based optimal design and synthesis of microelectromechanical systems. The genetic algorithm uses a hierarchical component-based genotype representation, which incorporates specific engineering knowledge into the design optimization process. Each microelectromechanical system component is represented by a gene with its own parameters defining its geometry and the way it can be modified from one generation to the next. The object-oriented genotype structures efficiently describe the hierarchical nature typical of engineering designs. They also encode knowledge-based constraints that prevent the genetic algorithm from wasting time exploring inappropriate regions of the search space. The efficiency of the hierarchical component-based genotype representation is demonstrated with surface-micromachined resonator designs.