Complexity in Genetic Programming: Using Entropy and Compression Metrics to Understand GP Behaviour

Created by W.Langdon from gp-bibliography.bib Revision:1.8178

@Unpublished{McKay:2007:isica,

• author = "R. I. (Bob) McKay",
• howpublished = "International Symposium on Intelligence, Computation and Applications",
• address = "Wuhan, China",
• month = sep,
• note = "Keynote Address",
• title = "Complexity in Genetic Programming: Using Entropy and Compression Metrics to Understand GP Behaviour",
• year = "2007",
• keywords = "genetic algorithms, genetic programming, bloat, size limit and introns, VC dimension",
• URL = "http://sc.snu.ac.kr/PAPERS/entcompress.pdf",
• notes = "'oppose convergence' eg 'Fitness sharing', 'Anticorrelation penalties', 'information based accuracy, parsimony', 'Disappointing results so far', 'Regular structure virtually never emerges in GP genotypes'.

  Generalisation see \cite{ieee-ec:Kushchu:2002}.

  'Equivalent Decision Simplification'.

  'Symbolic Regression' of cos(2x), '1000 runs, 500 population, 200 generations'.

  'Information theory to understand genetic programming'. 'Entropy of (binary?) subtrees' up to 4 nodes.

  'Compression and Regularity Regularity'. 'compressibility to measure complexity'. 'XMLPPM, an excellent tree compression algorithm'. 'Measure how much different runs discover the same building blocks'.",

}

Genetic Programming entries for R I (Bob) McKay

Citations