Foundations of genetic programming
Foundations of genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Parametric variability analysis for multistage analog circuits using analytical sensitivity modeling
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A general method to evaluate RF BIST techniques based on non-parametric density estimation
Proceedings of the conference on Design, automation and test in Europe
Evaluation of analog/RF test measurements at the design stage
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hierarchical parametric test metrics estimation: a ΣΔ converter BIST case study
ICCD'09 Proceedings of the 2009 IEEE international conference on Computer design
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CAFFEINE, introduced previously, automatically generates nonlinear, template-free symbolic performance models of analog circuits from SPICE data. Its key was a directly-interpretable functional form, found via evolutionary search. In application to automated sizing of analog circuits, CAFFEINE was shown to have the best predictive ability from among 10 regression techniques, but was too slow to be used practically in the optimization loop. In this paper, we describe Double-Strength CAFFEINE, which is designed to be fast enough for automated sizing, yet retain good predictive abilities. We design "smooth, uniform" search operators which have been shown to greatly improve efficiency in other domains. Such operators are not straightforward to design; we achieve them in functions by simultaneously making the grammar-constrained functional form implicit, and embedding explicit 'introns' (subfunctions appearing in the candidate that are not expressed). Experimental results on six test problems show that Double-Strength CAFFEINE achieves an average speedup of 5x on the most challenging problems and 3x overall; thus making the technique fast enough for automated sizing.