🚀 go-pugleaf

Einladung

Hiermit moechten wir auf die Gelegenheit aufmerksam machen, von einem
der wichtigsten Vertreter der Genetischen Algorithmen, Prof. David
Goldberg,
einen Vortrag zu seinem Fachgebiet zu hoeren. Der Vortrag ist oeffentlich
und Fachbesucher sind herzlich eingeladen. Die Vortragssprache ist
Englisch.

Michael Herdy

------------------------------------------------------------------------

Freitag, 7. November 1997
Fachgebiet Bionik und Evolutionstechnik
Ackerstr. 71-76
D-13355 Berlin (Wedding)
Zeit: Fr. 14:30-16:00
Ort:  H�rsaal: ACK 191
http://www.bionik.tu-berlin.de

Vortrag von  Prof. David E. Goldberg
  Department of General Engineering
                University of Illinois at Urbana-Champaign
                Urbana, IL 61801

Titel:          Toward a Mechanics of Innovation:
                Lessons from Genetic Algorithms


Abstract:

One of the attractions of recombinative genetic algorithms--search
procedures based on the mechanics of natural selection and genetics--is
that the processes involved seem intuitively similar to those of human
innovation, but that connection is not very helpful to the design of
genetic algorithms, because the processes and dynamics of innovation
are themselves not well understood.  In fact, the more useful line of
inquiry comes from reversing the chain of reasoning.  As we learn more
about the design of {\it competent} GAs---GAs that solve hard problems,
quickly, reliably, and accurately---it appears that the dynamics and
processes of innovation are succumbing to a more mechanistic understanding.

In this talk, we examine the implications of the design of competent
GAs for the understanding of recombinative innovation processes.
Starting from a brief introduction of the mechanics and effect of
simple GAs and moving toward the sixfold design decomposition that has
been used to develop competent GAs since 1993, a working, quantitative
theory of innovation is constructed.  The theory is predictive in
that calculation of critical "time" and "length" scales can be combined
with
dimensional reasoning to determine important dimensionless ratios such
as the {\it decision number} and the {\t innovation number} that help us
understand when and how well a innovative process will work and whether
those processes will scale to larger, more difficult situations.
Scalability will be demonstrated using examples from a number of competent
GAs,
including the fast messy GA, the gene-expression messy GA, and the
linkage-learning GA.  The talk closes with more speculative remarks
regarding the possibilities of a computational theory of the creative.