ACGP: Adaptable Constrained Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InCollection{janikow:2004:GPTP,

• author = "Cezary Z. Janikow",
• title = "{ACGP}: Adaptable Constrained Genetic Programming",
• booktitle = "Genetic Programming Theory and Practice {II}",
• year = "2004",
• editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and Bill Worzel",
• chapter = "12",
• pages = "191--206",
• address = "Ann Arbor, MI, USA",
• month = "13-15 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, representation, learning, adaptation, heuristics",
• ISBN = "0-387-23253-2",
• URL = "http://www.umsl.edu/cmpsci/about/People/Faculty/CezaryJanikow/untitled%20folder/ACGP.pdf",
• DOI = "doi:10.1007/0-387-23254-0_12",
• abstract = "Genetic Programming requires that all functions/terminals (tree labels) be given a priori. In the absence of specific information about the solution, the user is often forced to provide a large set, thus enlarging the search space often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming methods give users the power to specify some rules for valid tree construction, and thus to prune the otherwise unconstrained representation in which Genetic Programming operates. However, in general, the user may not be aware of the best representation space to solve a particular problem. Moreover, some information may be in the form of weak heuristics. In this work, we present a methodology, which automatically adapts the representation for solving a particular problem, by extracting and using such heuristics. Even though many specific techniques can be implemented in the methodology, in this paper we use information on local first-order (parent-child) distributions of the functions and terminals. The heuristics are extracted from the population by observing their distribution in better individuals. The methodology is illustrated and validated using a number of experiments with the 11-multiplexer. Moreover, some preliminary empirical results linking population size and the sampling rate are also given.",
• notes = "part of \cite{oreilly:2004:GPTP2}",

}

Genetic Programming entries for Cezary Z Janikow

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