Trustworthy Genetic Programming-Based Synthesis of Analog Circuit Topologies Using Hierarchical Domain-Specific Building Blocks

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{McConaghy:2012:ieeetec,

• author = "Trent McConaghy and Pieter Palmers and Michiel Steyaert and Georges G. E. Gielen",
• title = "Trustworthy Genetic Programming-Based Synthesis of Analog Circuit Topologies Using Hierarchical Domain-Specific Building Blocks",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2011",
• volume = "15",
• number = "4",
• pages = "557--570",
• month = aug,
• keywords = "genetic algorithms, genetic programming, Analog, Analog circuits, Design automation, Grammar, Integrated circuit modelling, Semiconductor process modeling, Solid modeling, Topology, design automation, evolutionary algorithm (EA), integrated circuit (IC), multiobjective optimisation",
• ISSN = "1089-778X",
• URL = "http://trent.st/content/2011-TEVC-mojito-ea.pdf",
• DOI = "doi:10.1109/TEVC.2010.2093581",
• size = "14 pages",
• abstract = "This paper presents MOJITO, a system that performs structural synthesis of analog circuits, returning designs that are trustworthy by construction. The search space is defined by a set of expert-specified, trusted, hierarchically-organised analog building blocks, which are organized as a parametrised context-free grammar. The search algorithm is a multiobjective evolutionary algorithm that uses an age-layered population structure to balance exploration versus exploitation. It is validated with experiments to search across more than 100000 different one-stage and two-stage opamp topologies, returning human-competitive results. The runtime is orders of magnitude faster than open-ended systems, and unlike the other evolutionary algorithm approaches, the resulting circuits are trustworthy by construction. The approach generalises to other problem domains which have accumulated structural domain knowledge, such as robotic structures, car assemblies, and modelling biological systems.",
• notes = "Also known as \cite{5699917}",

}

Genetic Programming entries for Trent McConaghy Pieter Palmers Michiel Steyaert Georges G E Gielen

Citations