Information Technology Reference
In-Depth Information
There are numerous other examples of error measures used as fitness functions.
For example, animated tile mosaics have been created that approach a reference
portrait over time (Ciesielski
2007
). The fitness of shape recognition modules have
been based on their ability to reproduce shapes in hand drawn samples (Jaskowski
2007
). An automated music improviser has been demonstrated that proceeds by er-
ror minimisation of both frequency and timbre information (Yee-King
2007
). Alsing
(
2008
) helped to popularise the error minimisation approach to mimetic rendering
with a project that evolved a version of the “Mona Lisa” using overlapping semi-
transparent polygons.
10.2.8 Automated Fitness Functions Based on Complexity
Measures
Fitness scores based on aesthetic quality rather than simple performance or mimetic
goals are much harder to come by. Machado and Cardoso's
NEvAr
system uses com-
putational aesthetic evaluation methods that attempt to meet this challenge. They
generate images using an approach first introduced by (Sims
1991
) called
evolv-
ing expressions
. It uses three mathematical expressions to calculate pixel values for
the red, blue, and green image channels. The set of math expressions operates as a
genotype that can reproduce with mutation and crossover operations.
Machado and Cardoso take a position related to Birkhoff's aesthetic measure.
The degree to which an image resists JPEG compression is considered an “image
complexity” measure. The degree it resists fractal compression is considered to be
proportional to the “processing complexity” that will tax an observer's perceptual
resources. Image complexity is then essentially divided by processing complexity
to calculate a single fitness value.
Machado and Cardoso reported surprisingly good imaging results using evolving
expressions with their complexity-based fitness function. But the authors were also
careful to note that their fitness function only considers one formulaic aspect of
aesthetic value. They posit that cultural factors ignored by NEvAr are critical to
aesthetics. In later versions of NEvAr a user guided interactive mode was added
(Machado and Cardoso
2002
;
2003
, Machado et al.
2005
, see also Chap. 11 in this
volume for their extended work in this vein).
10.2.9 Automated Fitness Functions in Evolutionary Music
Systems
For evolutionary music composition some have calculated fitness scores using only
evaluative rules regarding intervals, tonal centres, and compliance to key and meter.
Others, like
GenOrchestra
, are hybrid systems that also include some form of lis-
tener evaluation. The GenOrchestra authors note that unfortunately without human
