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actual phenotypes (those which have existed, or currently exist). In nature, what can
be successfully expressed by DNA is limited materially by physical constraints and
processes. In contrast to our Lisp expression example, once RNA and DNA were es-
tablished evolution has not really experimented with different self-replication mech-
anisms. We think of DNA as being a highly successful self-replicating molecule,
which might be true, but we have little to compare it with. Many factors affect the
variety
of life that has evolved on Earth. As evolution involves successful adapta-
tions, the changing environment of the Earth is an important factor in determining
evolutionary variety. In addition to geological events, environments change due to
presence of species and their interactions, a point that I will return to later in this
chapter.
2.2 Evolutionary Computing and Creativity
As noted in the previous section, EC methods (which include techniques such as Ge-
netic Algorithms, Evolutionary Strategies and Genetic Programming) have demon-
strated success in assisting users of complex creative systems to better locate re-
gions of high creative reward (Bentley and Corne
2002
, Romero et al.
2008
). In
broad terms they are “generate and test” algorithms that evolve a population of can-
didate solutions or artefacts. New, child artefacts are generated through random mu-
tation and/or recombination with selected parents. Populations are tested or ranked
by some measure, with the most highly valued individuals and their offspring more
likely to survive in subsequent generations. Incrementally, the overall “quality” of
the population
should
improve according to the fitness measure used. How well the
method does depends on many factors, including the nature of the fitness landscape
(determined in part by the representational scheme) and the evaluation of solution
fitness in artefacts. Success or otherwise is dependent on (i) the structure of the phe-
notype space, and (ii) the effectiveness of the fitness evaluation in determining the
quality of the artefacts produced.
5
Evolutionary approaches and aesthetic evaluation are reviewed extensively in the
chapter by Galanter (Chap. 10). So it is pertinent here to make just a few points.
Firstly, it is important to differentiate between an evolutionary system that gives
creative
results and one that generates
aesthetically pleasing
results. The former
does not preclude the latter, but they are in general, independent (i.e. it is possible
for a machine or algorithm to generate aesthetically pleasing images without that
system being creative). This distinction is often overlooked.
Some evolutionary systems use learnt or predefined measures of “creative” fea-
tures in their generated artefacts (Baluja et al.
1994
, Machado and Cardoso
2002
), or
rely on some form of aesthetic measure to evaluate an individual's fitness (Birkhoff
1933
, Staudek
2002
, Ramachandran
2003
, Svangåard and Nordin
2004
, Machado
et al.
2008
). Others use iterative human selection to rank individuals as part of the
5
This issue is a topic of discussion in Chap. 4.
