Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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In this work, the optimization of circuits design by using multiobjective evolutionary algorithm is addressed. This methodology enable to deal with circuit specifications -formulated as objective functions- that can be conflicting and want to be optimize at the same time. After the optimization process, a set of different trade-off solutions for the design of the circuit is obtained. This way, SPEA (Strength Pareto Evolutionary Algorithm) has been tested as optimizer of an hybrid CBL/CMOS configurable cell. As a result, some conclusions about the optimized values of the transistor sizes of this cell in order to minimized some power comsumption and delay timing specifications are obtained.