- 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 Computation",
- year = "2006",
- editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas",
- pages = "1060--1065",
- address = "Vancouver",
- month = "16-21 " # 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{"},",
