Strawman: 2, GP: 0

[Barak Pearlmutter]

It might be true that in a universe where everything was equally
likely, all search algorithms would be equally awful.  But that does
not appear to be the universe that we live it, so it is not
unreasonable to ask whether some particular search algorithm performs
well in practice.

But this point is somewhat moot, because Kevin Lang's recent post was
not claiming that "his" "new" algorithm was better than current
algorithms.

Lang published a well reasoned and careful scientific paper, which
cast doubt on the practicality and importance of a particular
algorithm (GP) by showing that on a very simple problem *taken from
the GP book* it compares unfavorably to a silly strawman algorithm.
This paper passed scientific muster, and was accepted into ML, a
respected refereed conference.

John Koza responded by distributing a lengthy unrefereed screed, the
bulk of which consisted of vicious invective and distorted
half-truths.  A small part of Koza's monograph had some actual
technical content: it gave some new numbers on a scaled-up version of
the problem in question, and interpreted them as showing that GP
scaled better than the algorithm Lang had compared it to.

However, Koza's interpretation was wrong: looking carefully at the raw
numbers shows that even in Koza's hands, GP is scaling horribly worse
than the silly strawman algorithm Lang had compared it to.

That is the point of Lang's recent post.