The application of genetic algorithms to the design of reconfigurable reasoning VLSI chips
FPGA '00 Proceedings of the 2000 ACM/SIGDA eighth international symposium on Field programmable gate arrays
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Promises and Challenges of Evolvable Hardware
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Genetic Algorithm-Based Methodology for Pattern Recognition Hardware
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
A Pattern Recognition System Using Evolvable Hardware
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Scalable Evolvable Hardware Applied to Road Image Recognition
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A Flexible and Efficient Hardware Architecture for Real-Time Face Recognition Based on Eigenface
ISVLSI '05 Proceedings of the IEEE Computer Society Annual Symposium on VLSI: New Frontiers in VLSI Design
On-Chip Evolution Using a Soft Processor Core Applied to Image Recognition
AHS '06 Proceedings of the first NASA/ESA conference on Adaptive Hardware and Systems
Gene finding using evolvable reasoning hardware
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
A flexible on-chip evolution system implemented on a xilinx Virtex-II pro device
ICES'05 Proceedings of the 6th international conference on Evolvable Systems: from Biology to Hardware
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Genetic Programming and Evolvable Machines
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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An evolvable hardware (EHW) architecture for high-speed pattern recognition has been proposed. For a complex face image recognition task, the system demonstrates (in simulation) an accuracy of 96.25% which is better than previously proposed EHW architectures. In contrast to previous approaches, this architecture is designed for online evolution. Incremental evolution and high level modules have been utilized in order to make the evolution feasible.