Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
CMOS: Circuit Design, Layout, and Simulation (IEEE Press Series on Microelectronic Systems)
CMOS: Circuit Design, Layout, and Simulation (IEEE Press Series on Microelectronic Systems)
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Transistor sizing for low power CMOS circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The design of resource efficient integrated circuits (ICs) requires solving a minimization problem which consists of more than one objective given as measures of the available resources. This multi-objective optimization problem (MOP) can be solved on the smallest unit of the IC, the standard cells, to improve the performance of the entire circuit. In this work, transistor sizing of an IC is approached via a multi-objective approach which includes the use of multi-objective evolutionary algorithms (MOEAs). We compare the performance of two MOEAs on a four-dimensional MOP of a particular standard cell. The results indicate that evolutionary strategies are suitable for the treatment of such problems and advantageous against other rather classical methods.