The Recombination Operator, its Correlation to the Fitness
Landscape and Search Performance
Greg Hornby Masters Thesis
Postscript available here
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
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.