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and system-produced images. Using famous paintings in the training set provided
stability and human-like standards of evaluation. Using system produced images al-
lowed the evolution of more discerning classifiers (Machado et al.
2008
). In a related
paper the authors demonstrate artificial neural networks that can discriminate works
between: Chopin and Debussy; Scarlatti and Purcell; Purcell, Chopin, and Debussy;
and other more complicated combinations. In another demonstration, a neural net-
work was able to discriminate works between Gauguin, Van Gogh, Monet, Picasso,
Kandinsky, and Goya (Machado et al.
2004
, Romero et al.
2003
).
Without explicit programming, artificial neural networks can learn and apply do-
main knowledge that may be fuzzy, ill defined, or simply not understood. Phon-
Amnuaisuk (
2007
) has used a type of artificial neural network called
self-organising
maps
to extract musical structure from existing human music, and then shape music
created by an evolutionary system by acting as a critic. Self-organising map-based
music systems sometimes produce reasonable sequences of notes within a measure
or two, but lack the kind of global structure we expect music to have. In an attempt to
address this problem self-organising maps have been organised in hierarchies so that
higher-level maps can learn higher levels of abstraction (Law and Phon-Amnuaisuk
2008
). In another experiment, artificial neural networks were able to learn viewer
preferences among Mondrian-like images and accurately predict preferences when
viewing new images (Gedeon
2008
).
10.2.4 Evolutionary Systems
The evolutionary approach to exploring solution spaces for optimal results has had
great success in a diverse set of industries and disciplines (Fogel
1999
). Across a
broad range of approaches some kind of evaluation is typically needed to steer evo-
lution towards a goal. Much of our discussion about computational aesthetic evalu-
ation will be in the context of evolutionary systems. But first consider the following
simplified industrial application.
Assume the problem at hand is the design of an electronic circuit. First, chromo-
some-inspired data structures are created and initially filled with random values.
Each chromosome is a collection of simulated genes. Here each gene describes an
electronic component or a connection, and each chromosome represents a circuit
that is a potential solution to the design problem. The genetic information is re-
ferred to as the
genotype
, and the objects and behaviours they ultimately produce
are collectively called the
phenotype
. The process of genotype-creating-phenotype
is called
gene expression
. A chromosome can reproduce with one or more of its
genes randomly mutated. This creates a variation of the parent circuit. Or two chro-
mosomes can recombine creating a new circuit that includes aspects of both parents.
In practice, a subset of chromosomes is selected for variation and reproduction,
and the system evaluates the children as possible solutions. In the case of circuit
design a chromosome will be expressed as a virtual circuit and then tested with a
software-based simulator. Each circuit design chromosome is assigned a score based
