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designing furniture (tables). Their fitness function sought to maximise height, sur-
face structure, and stability while minimising the amount of materials required. This
approach is similar to the optimisation-oriented evolutionary systems found in in-
dustry (Hornby and Pollack 2001 ).
Similarly, specific performance goals can provide a fitness function in a straight-
forward way in art applications. Sims' Evolved Virtual Creatures is an early exam-
ple. His evolutionary system bred virtual organisms with simple “neuron” circuitry
and actuators situated in a world of simulated physics. The initial creatures, seeded
with random genes, would typically just twitch in an uncoordinated way. But then
selection pressure was applied to the evolving population using a simple fitness
function that might reward jumping height, walking speed, or swimming mobility.
As a result, the evolved creatures exhibited very competent locomotion behaviour.
Some seemed to rediscover movement found in the natural world, while others ex-
hibited strange and completely novel solutions (Sims 1994 ).
Performance goals can also be useful in the development of characters for com-
puter games through evolution. For example, the amount of time a character survives
can be used as a fitness function yielding incrementally stronger play (Wu and Chien
2005 ).
Diffusion limited aggregation ( DLA ) systems can be used to create growing frost-
or fern-like patterns, and have been studied using evolutionary performance goals.
They grow as particles in random Brownian motion adhere to an initial seed parti-
cle. To study optimal seed placement, Greenfield ( 2008a ) applied an evolutionary
system where the size of the resulting pattern served as an effective fitness mea-
sure. In another project he used an evolutionary system to explore the effect of
transcription factors on morphology. Each transcription factor was assigned a dif-
ferent colour. The performance and aesthetics of the result were improved by using a
fitness function that rewarded transcription factor diversity (Greenfield 2004 ). Simi-
larly, an evolutionary sculpture system using cubic blocks as modules has produced
useful emergent forms simply by rewarding height or length (Tufte and Gangvik
2008 ).
In their project “Breed” Driessens and Verstappen created a subtractive sculpture
system. Each sculpture is started as a single cube treated as a cell. This cell is sub-
divided into eight smaller sub-cells, one for each corner. Rules driven by the state
of neighbouring cells determine whether a sub-cell is kept or carved away. Then
each of the remaining cells has the subdivision rules applied to them. And so on.
The final form is then evaluated for conformance to goals for properties such as vol-
ume, surface area and connectivity. In “Breed” the rule-set is the genotype, the final
sculpture is the phenotype, and evaluation relative to performance goals is used as
a fitness function. Unlike most other evolutionary systems there is a population size
of just one. A single mutation is produced and given an opportunity to unseat the
previous result. At some point the gene, i.e. rule set, ceases to improve by mutation
and the corresponding sculpture is kept as the result.
Whitelaw ( 2003 ) points out that unlike industrial applications where getting
stuck on a local maximum is seen as an impediment to global optimisation, this
project uses local maxima to generate a family of forms (differing solutions) related
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