Complexity Compression and Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Nordin:1995:cce,

• author = "Peter Nordin and Wolfgang Banzhaf",
• title = "Complexity Compression and Evolution",
• booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)",
• year = "1995",
• editor = "Larry J. Eshelman",
• pages = "310--317",
• 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://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/icga95-1.ps.gz",
• URL = "http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.2133",
• size = "8 pages",
• abstract = "Compression of information is an important concept in the theory of learning. We argue for the hypothesis that there is an inherent compression pressure towards short, elegant and general solutions in a genetic programming system and other variable length evolutionary algorithms. This pressure becomes visible if the size or complexity of solutions are measured without non-effective code segments called introns. The built in parsimony pressure effects complex fitness functions crossover probability, generality, maximum depth or length of solutions, explicit parsimony, granularity of fitness function, initialization depth or length, and modularization. Some of these effects are positive and some are negative. In this work we provide a basis for an analysis of these effects and suggestions to overcome the negative implications in order to obtain the balance needed for successful evolution. An empirical investigation that supports our hypothesis is also presented.",

}

Genetic Programming entries for Peter Nordin Wolfgang Banzhaf

Citations