Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
A Self-Biased High Performance Folded Cascode CMOS Op-Amp
VLSID '97 Proceedings of the Tenth International Conference on VLSI Design: VLSI in Multimedia Applications
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Analog circuit optimization system based on hybrid evolutionary algorithms
Integration, the VLSI Journal
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
Automated synthesis of analog electrical circuits by means ofgenetic programming
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Anaconda: simulation-based synthesis of analog circuits via stochastic pattern search
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The synthesis of CMOS operational amplifier (Op-Amp) can be translated into a constrained multi-objective optimization problem, in which a large number of specifications have to be taken into account, i.e., gain, unity gain-bandwidth (GBW), slew-rate (SR), common-mode rejection ratio (CMRR) and bias conditions. A constraint handling strategy without penalty parameters for multi-objective optimization algorithm is proposed. A standard operational amplifier is then designed, the results show the proposed methodology is very effective and can obtain better specifications than other methods.