Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InCollection{Mcconaghy:2008:GPTP,

• author = "Trent McConaghy and Pieter Palmers and Georges Gielen and Michiel Steyaert",
• title = "Automated Extraction of Expert Domain Knowledge from Genetic Programming Synthesis Results",
• booktitle = "Genetic Programming Theory and Practice {VI}",
• year = "2008",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• series = "Genetic and Evolutionary Computation",
• chapter = "8",
• pages = "111--125",
• address = "Ann Arbor",
• month = "15-17 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, synthesis, domain knowledge, multi-objective, data mining, analog, integrated circuits, age layered population structure",
• DOI = "doi:10.1007/978-0-387-87623-8_8",
• URL = "http://trent.st/content/2008-GPTP-synthesis_insight.pdf",
• size = "14 pages",
• isbn13 = "978-0-387-87622-1",
• abstract = "Recent work in genetic programming shows how expert domain knowledge can be input to a genetic programming (GP) synthesis system, to speed it up by orders of magnitude and give trustworthy results. On the flip side, this paper shows how expert domain knowledge can be output from the results of a synthesis run, in forms that are immediately recognisable and transferable for problem domain experts. Specifically, using the application of analog circuit design, this paper presents a methodology to automatically generate a decision tree for navigating from performance specifications to topology choice; a means to extract the relative importances of topology and parameters on performance; and to generate whitebox models that capture tradeoffs among performances. The extraction uses a combination of data-mining and genetic programming technologies. This paper also presents techniques to ensure that the GP-based synthesis system can indeed create a richly-populated, high-performance dataset, including: a parallel-computing, multi-objective age-layered population structure (ALPS) for fast and reliable convergence; average ranking on Pareto fronts (ARF) to handle many objectives; and generating good initial topology sizings via multigate constraint satisfaction. Results are shown on operational amplifier synthesis across thousands of topologies that generated a database containing thousands of Pareto-optimal designs across five objectives and dozens of constraints.",
• notes = "part of \cite{Riolo:2008:GPTP} published in 2009",

}

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

Citations