No experiment is ever a complete failure, it can always serve as a bad
example. From a failed study we cannot exactly learn what we intended to but
we can always learn something. This shall be the mission statement of this
Failures frequently happen in experimental as well as in theoretical research.
The question is what we can learn from a specific failure, and on a higher
level, how to deal with these failures. The main emphasis is thus twofold: To
inform about concrete failed approaches, and to review and develop methods of
attaining progress beyond the failures.
Participants will present seemingly clever ideas and concepts that somehow did
not work. These can e.g. be the beginning of the train of thoughts which
already led to another successful approach, or it may be the presentation of a
problem which is still unsolved. Sharing experiences on failures will help
understanding e.g. a certain method and its area of application, a certain
problem, or the relation between an approach and a problem. Learning that a
certain method for theoretical analysis did not provide the desired insights
may give an impression for which kind of problems and questions it is suitable.
For experimental works, there is a strengthening movement of refining the
methodology with regard to parameter analysis and enhanced statistics.
It is intended that differences and similarities of the current scientific
approaches of several researchers are discussed, as well as the possibilities
of 'negative papers'. This relates to many issues in the philosophy of science
and may contribute to a further development of experimental and theoretical
methodologies in evolutionary computation.
Hans-Paul Schwefel, TU Dortmund University Failures as Stepping Stones to Success or "per aspera ad astra"
The implicit thesis of this talk's title will be
underpinned with some examples from (my) real life.
A first example leads back to the 1960s, when I
simulated the (1+1)-ES with discrete mutations on
a two-dimensional parabolic ridge by means of a Z23
computer. The result - getting stuck in certain
search directions - led to making use of gaussian
The second example comes from experimental investigations
to determine the shape of a hot water flashing nozzle,
the water being really hot and not simulated on a computer.
In search for a multimembered evolutionary algorithm with
effective self-adaptation of the mutation strengths, a
couple of failures occurred. These, however, rendered
deep insight into basic prerequisites to achieve the goal.
And finally, some theory will be re-presented about
the optimal failure rate in two black-box situations.
Pier Luca Lanzi, Politecnico di Milano Compete to Win, Lose to Learn - Experiences from Game Competitions
We invite submissions in the range of short descriptions of an
interesting enlightening failure up to full papers.
Please send your submissions (2-8 pages in ACM style) to:
Accepted papers will be published in the GECCO 2009 companion material
and be included with the proceedings on a CD, and also in the ACM Digital library.
Submissions due: April 3, 2009 (extended)
Decision notifications: April 10, 2009
Camera ready version due: April 17, 2009
Registration for presenting authors due: April 27, 2009
GECCO 2009 conference: July 8-12, 2009 Workshop: July 8, 14:00 - 18:00
Science must begin with myths, and with the criticism of myths.
Whenever a theory appears to you as the only possible one, take this as a sign that you have neither understood the theory nor the problem which it was intended to solve.
With engineering, I view this year's failure as next year's opportunity to
try it again. Failures are not something to be avoided. You want to have
them happen as quickly as you can so you can make progress rapidly.
Failure is only the opportunity to begin again more intelligently.
There is no failure. Only feedback.
I have not failed. I've just found 10,000 ways that won't work.
Try again. Fail again. Fail better.