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by their shared fitness function. Also Whitelaw points out that unlike some genera-
tive systems that reflect human selection and intent, Driessens and Verstappen have
no particular result in mind other than allowing the system to play itself out to a
final self-directed result. In this case performance goals play quite a different role
than those used in optimisation-oriented industrial systems.
10.2.7 Evolutionary Fitness Measured as Error Relative
to Exemplars
Representationalism in visual art began diminishing in status with the advent of pho-
tographic technologies. Other than use as an ironic or conceptual gesture, mimesis is
no longer a highly valued pursuit in contemporary visual art. Similarly a difference
or error measure comparing a phenotype to a real-world example is not typically
useful as an aesthetic fitness function. In the best case such a system would merely
produce copies. What have proven interesting, however, are the less mimetic in-
termediate generations where error measures can be reinterpreted as the degree of
abstraction in the image.
For example, Aguilar and Lipson (
2008
) constructed a physical painting machine
driven by an evolutionary system. A scanned photograph serves as the target and
each chromosome in the population is a set of paint stroke instructions. A model of
pigment reflectance is used to create digital simulations of the prospective painting
in software. A software comparison of pixel values from the simulated painting
and the original image generates a fitness score. When a sufficient fitness score is
achieved the chromosome is used to drive a physical painting machine that renders
the brush strokes on canvas with acrylic paint.
Error measurement makes particularly good sense when programming music
synthesisers to mimic other sound sources. Comparisons with recordings of tra-
ditional acoustic instruments can be used as a fitness function. And before the evo-
lutionary system converges on an optimal mimesis interesting timbres may be dis-
covered along the way (McDermott et al.
2005
, Mitchell and Pipe
2005
).
Musique concrete is music constructed by manipulating sound samples. For evo-
lutionary musique concrete short audio files can be subjected to operations similar
to mutation and crossover. They are then combined and scored relative to a sec-
ond target recording. Again mimesis is not the intent. What the audience hears is
the evolving sound as it approaches but does not reach the target recording (Mag-
nus
2006
, Fornari
2007
). Gartland-Jones (
2002
) has used a similar target tracking
approach with the addition of music theory constraints for evolutionary music com-
position.
In a different music application Hazan et al. (
2006
) have used evolutionary meth-
ods to develop regression trees for expressive musical performance. Focusing on
note duration only, and using recordings of jazz standards as a training set, the re-
sulting regression trees can be used to transform arbitrary flat performances into
expressive ones.
