Evolving hardware with genetic learning: a first step towards building a Darwin machine
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Following the path of evolvable hardware
Communications of the ACM
The GRD Chip: Genetic Reconfiguration of DSPs for Neural Network Processing
IEEE Transactions on Computers
Evolution of Analog Circuits on Field Programmable Transistor Arrays
EH '00 Proceedings of the 2nd NASA/DoD workshop on Evolvable Hardware
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Genetic Programming and Evolvable Machines
Connection Science - Evolutionary Learning and Optimisation
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Using negative correlation to evolve fault-tolerant circuits
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Research on the online evaluation approach for the digital evolvable hardware
ICES'07 Proceedings of the 7th international conference on Evolvable systems: from biology to hardware
Research on multi-objective on-line evolution technology of digital circuit based on FPGA model
ICES'07 Proceedings of the 7th international conference on Evolvable systems: from biology to hardware
Research on fault-tolerance of analog circuits based on evolvable hardware
ICES'07 Proceedings of the 7th international conference on Evolvable systems: from biology to hardware
Promises and challenges of evolvable hardware
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Brushless Motors are frequently employed in control systems. The reliability of the brushless motor control circuits is highly critical especially in harsh environments. This paper presents an Evolvable Hardware (EHW) platform for automated design and adaptation of a brushless motors control circuit. The platform uses the principles of EHW to automate the configuration of FPGA dedicated to the implementation of the motor control circuit. The ability of the platform to adapt to a certain number of faults was investigated through introducing single logic unit faults and multi-logic unit faults. Results show that the functionality of the motor control circuit can be recovered through evolution. They also show that the location of faulty logic units can affect the ability of the evolutionary algorithm to evolve correct circuits, and the evolutionary recovery ability of the circuit decreases as the number of fault logic units is increasing.