The Y-Test: Fairly Comparing Experimental Setups with Unequal Effort

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Steffen Christensen and Franz Oppacher",
  title =        "The Y-Test: Fairly Comparing Experimental Setups with
                 Unequal Effort",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "1060--1065",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/CEC.2006.1688330",
  size =         "6 pages",
  abstract =     "Evolutionary Computation has been dogged by a central
                 statistical issue: how does one fairly compare the
                 performance of two techniques which differ in the
                 amount of work required? While Koza's computational
                 effort statistic attempts to answer this problem, it is
                 a point statistic and has other statistical problems.
                 We present the y-test, a statistical test which takes
                 as input a set of outcomes from the observed runs of
                 two processes A and B. The y-test synthetically
                 performs a work-balanced comparison between k runs of A
                 and l runs of B. We show that by choosing k and l
                 appropriately, we can compensate for the fact that one
                 of the processes is computationally more efficient than
                 the other.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
                 = {"}356--361{"},",

Genetic Programming entries for Steffen Christensen Franz Oppacher