Abstract
This paper investigates the application of genetic programming to problems in industrial analog computer-aided design (CAD). One CAD subdomain, analog structural synthesis, is an often-cited success within the genetic programming (GP) literature, yet industrial use remains elusive. We examine why this is, by drawing upon our own experiences in bringing analog CAD tools into industrial use. In sum, GP-synthesized designs need to be more robust in very specific ways. When robustness is considered, a GP methodology of today on a reasonable circuit problem would take 150 years on a 1,000-node 1-GHz cluster. Moore’s Law cannot help either, because the problem itself is ‘Anti-Mooreware’ — it becomes more difficult as Moore’s Law progresses. However, we believe the problem is still approachable with GP; it will just take a significant amount of ‘algorithm engineering.’ We go on to describe the recent application of GP to two other analog CAD subdomains: symbolic modeling and behavioral modeling. In contrast to structural synthesis, they are easier from a GP perspective, but are already at a level such that they can be exploited in industry. Not only is GP the only approach that gives interpretable SPICE-accurate nonlinear models, it turns out to outperform nine other popular blackbox approaches in a set of six circuit modeling problems.
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McConaghy, T., Gielen, G. (2006). Genetic Programming in Industrial Analog CAD: Applications and Challenges. In: Yu, T., Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice III. Genetic Programming, vol 9. Springer, Boston, MA. https://doi.org/10.1007/0-387-28111-8_19
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DOI: https://doi.org/10.1007/0-387-28111-8_19
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