The Recombination Operator, its Correlation to the Fitness
Landscape and Search Performance
This thesis was completed at the
University of Alberta's,
Computing Science Department under the supervision of
Joe Culberson.
(ghornby.ps.Z 1.08M)
Abstract
A common misconception in evolutionary algorithms (EAs) is that one
recombination operator is universally better than another.
In fact, a recombination operator will only get better performance
on a function if it incorporates some knowledge about that
function -- called tuning it to the function's fitness
landscape. In this thesis we identify three ways in which a
recombination operator can be tuned to a real-valued
landscape: distance, directionality, and distributional bias.
We empirically show that a directionally tuned recombination
operator gives better search performance than an untuned
operator.
We also show that a recombination operator that is tuned
to one landscape can be mis-tuned to a similar landscape.
In addition we find several surprises that contradict our
initial intuition but yield to further analysis. For example
one interesting observation is a decrease in the number of
individuals on the global optimum. We show this to be
caused by the attractive pull of a larger group of individuals on
a peak with a larger basin of attraction.
homepage/top,
Brandeis University,
Computer Science Department
hornby@cs.brandeis.edu -- Last modified: Oct. 18, 1996