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Abstract: Informally GA-hardness asks what makes a problem hard or easy for Genetic Algorithms (GAs) to optimize. Characterizing GAhardness has received significant attention since the invention of GAs, yet it remains quite open. In this paper, we first present an abstract, general framework of problem (instance) hardness and algorithm performance for search based on Kolmogorov complexity. We also show, by Rice's theorem, the nonexistence of a predictive GA-hardness measure based only on the description... (Update)
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BibTeX entry: (Update)
@inproceedings{ guo:2003:gecco,
author = "Haipeng Guo and William H. Hsu",
title = "{GA}-Hardness Revisited",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D.
Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and
R. Standish and G. Kendall and S. Wilson and M. Harman
and J. Wegener and D. Dasgupta and M. A. Potter and A.
C. Schultz and K. Dowsland and N. Jonoska and J.
Miller",
year = "2003",
pages = "1584--1585",
address = "Berlin",
publisher = "Springer-Verlag",\
note = "\url{http://citeseer.ist.psu.edu/guo03gahardness.html}",
url = "citeseer.ist.psu.edu/guo03gahardness.html" }
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Documents on the same site (http://www.cis.ksu.edu/~hpguo/publications.html):
A Bayesian Approach for Automatic Algorithm Selection - Guo (Correct)
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