Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
DARWIN: CMOS opamp synthesis by means of a genetic algorithm
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Introduction to VLSI Systems
Automated Analog Circuit Sythesis Using a Linear Representation
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Analog and Digital Circuit Design in 65 nm CMOS: End of the Road?
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Analog Design Essentials (The International Series in Engineering and Computer Science)
Analog Design Essentials (The International Series in Engineering and Computer Science)
Simultaneous multi-topology multi-objective sizing across thousands of analog circuit topologies
Proceedings of the 44th annual Design Automation Conference
A synthesis system for analog circuits based on evolutionary search and topological reuse
IEEE Transactions on Evolutionary Computation
Analog Genetic Encoding for the Evolution of Circuits and Networks
IEEE Transactions on Evolutionary Computation
The invention of CMOS amplifiers using genetic programming and current-flow analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Integer programming based topology selection of cell-level analog circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Importance Sampled Circuit Learning Ensembles (ISCLEs) is a novel analog circuit topology synthesis method that returns designer-trustworthy circuits yet can apply to a broad range of circuit design problems including novel functionality. ISCLEs uses the machine learning technique of boosting, which does importance sampling of "weak learners" to create an overall circuit ensemble. In ISCLEs, the weak learners are circuit topologies with near-minimal transistor sizes. In each boosting round, first a new weak learner topology and sizings are found via genetic programming-based "MOJITO" multi-topology optimization, then it is combined with previous learners into an ensemble, and finally the weak-learning target is updated. Results are shown for the trustworthy synthesis of a sinusoidal function generator, and a 3-bit A/D converter.