Causality in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Rosca:1995:cause,

• author = "Justinian Rosca and Dana H. Ballard",
• title = "Causality in Genetic Programming",
• booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)",
• year = "1995",
• editor = "Larry J. Eshelman",
• pages = "256--263",
• address = "Pittsburgh, PA, USA",
• publisher_address = "San Francisco, CA, USA",
• month = "15-19 " # jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-55860-370-0",
• URL = "ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/95.icga.causality.ps.gz",
• size = "8 pages",
• abstract = "Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting control parameters for speeding up GP search. We first analyze the effects of crossover to show the weak causality of the GP representation and operators. Hierarchical GP approaches based on the discovery and evolution of functions amplify this phenomenon. However, selection gradually retains strongly causal changes. Causality is correlated to search space exploitation and is discussed in the context of the exploration-exploitation tradeoff. The results described argue for a bottom-up GP evolutionary thesis. Finally, new developments based on the idea of GP architecture evolution [Koza94] are discussed from the causality perspective.",
• notes = "\cite{Rosca:1995:aHGP} is a longer version of this paper",

}

Genetic Programming entries for Justinian Rosca Dana H Ballard

Citations