Causality of Hierarchical Variable Length Representations

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@InProceedings{igel:98,

• author = "Christian Igel",
• title = "Causality of Hierarchical Variable Length Representations",
• booktitle = "Proceedings of the 1998 IEEE World Congress on Computational Intelligence",
• year = "1998",
• pages = "324--329",
• address = "Anchorage, Alaska, USA",
• month = "5-9 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, coding, hierarchical variable-length representations, problem difficulty, program tree representations, quantitative probabilistic causality measure, search space metric, statistical fitness landscape analysis, strong causality, tree edit distance, probability, program control structures, programming theory, tree searching",
• ISBN = "0-7803-4869-9",
• URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/igel/CoHVLR.ps.gz",
• DOI = "doi:10.1109/ICEC.1998.699753",
• size = "6 pages",
• abstract = "In this paper, the strong causality of program tree representations is considered. A quantitative, probabilistic causality measure is used in contrast to statistical fitness landscape analysis methods. Although it fails to rank different problems according to their difficulty, it is helpful for choosing the right coding for a given task. The investigation uses a metric on the search space called the tree edit distance. Different ways to define such a measure are discussed.",
• notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE World Congress on Computational Intelligence",

}

Genetic Programming entries for Christian Igel

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