Lipschitzian optimization without the Lipschitz constant
Journal of Optimization Theory and Applications
Multicriteria fuzzy control using evolutionary programming
Information Sciences: an International Journal
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
MOSFET Modeling and Bsim3 User's Guide
MOSFET Modeling and Bsim3 User's Guide
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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
Multi-objective optimization of doping profile in semiconductor design
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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In this work, we compare evolutionary algorithms and standard optimisation methods on two circuit design problems: the parameter extraction of a device circuit model and the multiobjective optimisation of an operational transconductance amplifier. The comparison is made in terms of quality of the solutions and computational effort, that is, objective function evaluations needed to compute them. The experimental results obtained show that standard techniques are more robust than evolutionary algorithms, while the latter are more effective in terms of the standard metrics and function calls. In particular for the multiobjective problem, the observed Pareto front determined by evolutionary algorithms has a better spread of solutions with a larger number of non-dominated solutions when compared to the standard multiobjective techniques.