- author = "Astro Teller and Manuela Veloso",
- title = "Program Evolution for Data Mining",
- editor = "Sushil Louis",
- publisher = "JAI Press",
- journal = "The International Journal of Expert Systems",
- year = "1995",
- volume = "8",
- number = "3",
- pages = "216--236",
- keywords = "genetic algorithms, genetic programming, memory",
-
URL = "
http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Astro-ESJ.ps",
-
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Astro-ESJ.ps.Z",
- size = "21 pages",
-
abstract = "Around the world there are innumerable databases of
information. The quantity of information available has
created a high demand for automatic methods for
searching these databases and extracting specific kinds
of information. Unfortunately, the information in these
databases increasingly contains signals that have no
corresponding classification symbols. Examples include
databases of images, sounds, etc. A few systems have
been written to help solve these search and retrieve
issues. But we can not write a new system for every
kind of signal we want to recognize and extract. Some
work has been done on automating (i.e. learning) the
task of identifying desired signal elements. It would
be useful to automate (learn) not just a part of the
classification function, but the entire signal
identification program. It would be helpful if we could
use the same learning architecture to automatically
create these programs for distinguishing many different
classes of the same signal type. It would be better
still if we could use the same learning architecture to
create these programs even for signal types as
different as images and sound waves. We introduce PADO
(Parallel Architecture Discovery and Orchestration), a
learning architecture designed to deliver this. PADO
has at its core a variant of genetic programming (GP)
that extends the paradigm to explore the space of
algorithms. PADO learns the entire classification
algorithm for an arbitrary signal type with arbitrary
signal class distinctions. This architecture has been
designed specifically for signal understanding and
classification. The architecture of PADO and its
achievements on the recovery of visual and acoustic
signal classes from test databases are the subjects of
this article.
",
- notes = "Third Quarter. Special Issue on Genetic Algorithms and Knowledge Bases.",
