Biomedical Engineering Reference
In-Depth Information
In music, for example, GP was used to extract
structural information from musical composi-
tion in order to model the process so that auto-
matic composition of music pieces becomes
possible [92] .
Many of these problems require a huge
amount of computational power on the part of
the GP systems. Parallel evolution has hence
been a key engineering aspect of developments
in GP. As a paradigm, GP is very well suited for
a natural way of parallelization. With the advent
of inexpensive parallel hardware in recent years,
in particular through graphics processing units
[93-95] , a considerable proliferation of results is
expected from GP systems [96] .
2. The result is equal to or better than a result
that was accepted as a new scientific result
at the time when it was published in a
peer-reviewed scientific journal.
3. The result is equal to or better than a
result that was placed into a database or
archive of results maintained by an inter-
nationally recognized panel of scientific
experts.
4. The result is publishable in its own right as
a new scientific result, independent of the
fact that the result was mechanically
created.
5. The result is equal to or better than the
most recent human-created solution to a
long-standing problem for which there has
been a succession of increasingly better
human-created solutions.
6. The result is equal to or better than a
result that was considered an achievement
in its field at the time it was first
discovered.
7. The result solves a problem of indisputable
difficulty in its field.
8. The result holds its own or wins a
regulated competition involving human
contestants (in the form of either live
human players or human-written
computer programs).
17.6 HUMAN-COMPETITIVE
RESULTS OF GENETIC
PROGRAMMING
In the last decade, a substantial number of results
have been published in various fields that claim
to have produced human-competitive results
by the application of GP as a problem-solving
method [97] .
These claims are based on a comparison
between the currently best-known human solu-
tions to a problem and their respective counter-
parts produced by GP. Applications are from
areas such as quantum computing algorithms,
analog electrical circuit design and other mechan-
ical and electrical designs, game-playing appli-
cations, finite algebras and other mathematical
systems, bioinformatics and other scientific pat-
tern recognition problems, reverse engineering
of systems, and empirical model discovery.
The claims of human-competitiveness are
based on criteria that Koza et al. proposed in 2003:
Some of the similarities of these successes
have been summarized by Koza [73] as follows:
• Usually,alargeamountofcomputational
power has to be invested in order to gain
human-competitive results from GP runs.
• Mosttimes,adedicatedrepresentationfor
the solution, known to be efficient by the
specialist, has been applied to allow the full
power of expression of solutions to be born
on the problem.
• TheGPsystemhasbeenequippedwith
dedicated growth or development operators
such that the adaptation of complexity of a
description can be achieved smoothly.
1. The result was patented as an invention in
the past, is an improvement over a pat-
ented invention, or would qualify today as
a patentable new invention.
 
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