Improving the Scalability of Generative Representations

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InCollection{Hornby:2007:GPTP,

• author = "Gregory S. Hornby",
• title = "Improving the Scalability of Generative Representations",
• booktitle = "Genetic Programming Theory and Practice {V}",
• year = "2007",
• editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
• series = "Genetic and Evolutionary Computation",
• chapter = "8",
• pages = "127--144",
• address = "Ann Arbor",
• month = "17-19" # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-0-387-76308-8",
• DOI = "doi:10.1007/978-0-387-76308-8_8",
• size = "17 pages",
• abstract = "With the recent examples of the human-competitiveness of evolutionary design systems, it is not of interest to scale them up to produce more sophisticated designs. Here we argue that for computer-automated design systems to scale to producing more sophisticated results they must be able to produce designs with greater structure and organisation. By structure and organization we mean the characteristics of modularity, reuse and hierarchy (MR&H), characteristics that are found both in man-made and natural designs. We claim that these characteristics are enabled by implementing the attributes of combination, control-flow and abstraction in the representation, and define metrics for measuring MR&H and define two measures of overall structure and organisation by combining the measures of MR&H. To demonstrate the merit of our complexity measures, we use an evolutionary algorithm to evolve solutions to different sizes for a table design problem, and compare the structure and organisation scores of the best tables against existing complexity measures. We find that our measures better correlate with the complexity of good designs than do others, which supports our claim that MR&H are important components of complexity. We also compare evolution using five representations with different combinations of MR&H, and find that the best designs are achieved when all three of these attributes are present. The results of this second set of experiments demonstrate that implementing representations with MR&H can greatly improve search performance.",
• notes = "part of \cite{Riolo:2007:GPTP} Published 2008",
• affiliation = "NASA Ames Research Center U. C. Santa Cruz, Mail Stop 269-3 Moffett Field CA 94035",

}

Genetic Programming entries for Gregory S Hornby

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