Computational Aesthetic Evaluation: Past and Future - Computers and Creativity - page 262

evaluation “the produced tunes do not yet correspond to a really human-like musical
composition” (Khalifa and Foster 2006 , De Felice and Fabio Abbattista 2002 ).
Others have used music theory-based fitness functions for evolutionary bass har-
monisation (De Prisco and Zaccagnino 2009 ), or to evolve generative grammar ex-
pressions for music composition (Reddin et al. 2009 ). For mimetic evolutionary
music synthesiser programming McDermott et al. ( 2005 ) used a combination of
perceptual measures, spectral analysis, and sample-level comparison as a fitness
function to match a known timbre.
Weinberg et al. ( 2009 ) have created a genetically based robotic percussionist
named Haile that can “listen” and trade parts in the call and response tradition.
Rather than starting with a randomised population of musical gestures Haile begins
with a pool of pre-composed phrases. This allows Haile to immediately produce
musically useful responses. As Haile runs, however, the evolutionary system will
create variations in real time. The fitness function used for selection uses an algo-
rithm called dynamic time warping .
Dynamic time warping here provides a way to measure the similarity between
two sequences that may differ in length or tempo. In response to a short rhythmic
phrase played by a human performer, Haile applies the dynamic time warping-based
fitness function to its population of responses and then plays back the closest match.
The goal is not to duplicate what the human player has performed, but simply to craft
a response that is aesthetically related and thus will contribute to a well-integrated
performance.
10.2.10 Multi-objective Aesthetic Fitness Functions
in Evolutionary Systems
Aesthetic judgements are typically multidimensional. For example, evaluating a tra-
ditional painting involves formal issues regarding colour, line, volume, balance, and
so on. A fitness function that has to include multiple objectives like these will typ-
ically have a sub-score for each. Each sub-score will be multiplied by its own co-
efficient that serves as a weight indicating its relative importance. The weighted
sub-scores are then summed for a final total score.
However, the weights are typically set in an ad hoc manner, and resulting evalua-
tions may not push the best work to the front. And there is no reason to assume that
the weights should maintain a static linear relationship regardless of the sub-score
values. For example, various aspects of composition may influence the relative im-
portance of colour.
Pareto ranking can address some of these concerns as an alternative to simple
weights. In Pareto ranking one set of scores is said to dominate another if it is
at least as good in all component sub-scores, and better in at least one. A rank 1
set of scores is one that isn't dominated. When there are multiple objectives there
will typically be multiple rank 1 sets of scores. The dimension of the problem they
dominate is what differentiates rank 1 genotypes, and all can be considered viable.