DARWIN: CMOS opamp synthesis by means of a genetic algorithm
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
MAELSTROM: efficient simulation-based synthesis for custom analog cells
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Evolutionary computation and Wright's equation
Theoretical Computer Science - Natural computing
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Application of the Univariate Marginal Distribution Algorithm to Analog Circuit Design
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
The equation for response to selection and its use for prediction
Evolutionary Computation
A comparison of different circuit representations for evolutionary analog circuit design
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
A circuit representation technique for automated circuit design
IEEE Transactions on Evolutionary Computation
Synthesis of high-performance analog circuits in ASTRX/OBLX
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
BLADES: an artificial intelligence approach to analog circuit design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
OASYS: a framework for analog circuit synthesis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
OPASYN: a compiler for CMOS operational amplifiers
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Anaconda: simulation-based synthesis of analog circuits via stochastic pattern search
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
AMGIE-A synthesis environment for CMOS analog integrated circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The paper represents the approach to evolutionary analogue circuit design on the base of the univariate marginal distribution algorithm. In order to generate a new population the probability distribution is used instead of reproduction operators. It allows us to control evolvability of a population on mesoscopic level. Experimental results obtained have indicated that a high mutation rate increases the success rate, although computational costs are increased too. The effective mutation rate that supplies high success rate and small computational costs is examined for different weightings of the fitness function.