Computational Aesthetic Evaluation: Past and Future - Computers and Creativity - page 258

of systems around the world. Users are invited to provide online feedback regarding
their preferences (Draves 2005 ).
But the crowd-sourcing solution is not without its own potential problems. Artists
Komar and Melamid executed a project called The People's Choice that began by
polling the public about their preferences in paintings. Based on the results regard-
ing subject matter, colour, and so on they created a painting titled America's Most
Wanted . The result is a bland landscape that would be entirely unmemorable if it
were not for the underlying method and perhaps the figure of George Washington
and a hippopotamus appearing as dada-like out-of-context features. As should be
expected the mean of public opinion doesn't seem to generate the unique vision
most expect of contemporary artists. Komar and Melamid's critique in this project
was directed at the politics of public relations and institutions that wield statistics
as a weapon. But the aesthetic results advise caution to those who would harness
crowd-sourced aesthetic evaluation in their art practice (Komar et al. 1997 ,Ross
1995 ). It's also worth noting that Melamid observed that some aesthetic preferences
are culturally based but others seemed to be universal. The evolutionary implica-
tions of this will be discussed later in the section on Denis Dutton and his notion of
the “art instinct”, Sect. 10.3.1 .
Another approach has been to manually score a subset, and then leverage that
information across the entire population. Typically this involves clustering the pop-
ulation into similarity groups, and then only manually scoring a few representatives
from each (Yuan 2008 , Machado et al. 2005 ). Machwe ( 2007 ) has suggested that
artificial neural networks can generalise with significantly fewer scored works than
the interactive approach requires.
10.2.6 Automated Fitness Functions Based on Performance Goals
The Mechanical Turk was a purported mechanical chess-playing machine created
in the late 18th century by Wolfgang von Kempelen. But it was really more a feat
of stage magic than computation. Exhibitors would make a great show of opening
various doors revealing clockwork-like mechanisms. Despite appearances, a human
operator was hidden inside the cabinet, so the chess game was won or lost based on
the decisions the operator made (Aldiss 2002 , Standage 2002 ).
To some extent using interactive evolutionary computing for art is a similar trick.
These systems can generate and display a variety of options at every step, but ul-
timately the aesthetic challenge is won or lost based on the decisions made by the
artist-operator.
Fully automated evolutionary art systems call for, rather than offer, a solution
to the challenge of computational aesthetic evaluation. Machine evaluation can be
relatively simple when the aesthetic is Louis H. Sullivan's principle that “form fol-
lows function” (Sullivan 1896 ). Computational evaluation here is tractable to the
extent the needed functionality can be objectively evaluated via computation. For
example, Gregory Hornby and Jordan Pollack created an evolutionary system for