Biomedical Engineering Reference
In-Depth Information
esthetic media [
2
]. The idea which is to be modeled is initially identi
ed and is
implemented as a program via the abstract rules framed at the time of design. The
program when run gets converted into a stream of binary numbers indicating
complex geometry which in turn gives the illusion of depth and space. The primary
questions which are to be answered during the developmental scenario tend to be
the following:
cial life
that evolves toward some subjective criteria of audience experiencing it without
them needing to explicitly perform
rstly knowing how a designer exactly creates a virtual arti
tness selection, and secondly how the rela-
tionship between real and virtual spaces is acknowledged in a way that the spaces
are integrated phenomenally.
The arti
cial life environment operates over a cellular lattice inhabited by agents
who use an internal, evolvable, rule-based system to control the external environ-
mental behavior. The use of genetic algorithms evolves toward
nding maxima in
tness which is individually evaluated for each phenotype of the population [
1
].
Arti
rst used by the computer scientist Chris Langton in 1986 to
describe human-made systems that describe characteristics in biological systems.
The domain is concerned with generating lifelike behavior using computer simu-
lations or building robots [
2
]. According to Langton, living system exists inde-
pendently and has to be rede
cial life is a term
ned based on the instance of life as we know it or a
more general prediction of life as it could be. Life is here de
ned by the mecha-
nisms and behaviors that might be realized in other media such as silicon. Although
silicon-based life sounds profoundly immoral, it rede
nes what life is and what life
could be. Emergence is the central concept of arti
cial life which explains the
crucial leap it makes between life and non-life [
8
]. The idea of emergence began
with simple local rules through local interaction and feedback. Feedback allows
complexity to emerge spontaneously via recursive looping which magni
es devi-
ations and derives complex interactions and unpredictable evolutions associated
with emergence [
9
].
Cormack
[
7
] has already been quoted before in the
paper. Dawkins proposed the biomorph model [
10
] where he used a recursive tree
drawing program to draw a single vertical line. The line further splits into two sub-
branches, and the process continues iteratively from generation to generation. All
the sub-branches are mutations of the parent branches. The degree of mutation
depends on the numeric value that controls the length and the direction of sub-
branches. Sims [
11
] used mathematics equations to generate images and said that
arti
'
s sonic Ecosystem
“
Eden
”
cial evolution has the potential to achieve
fl
exible complexity. He explored the
dif
culty in automatically exploring esthetic success of a genetic image. His success
allowed the user to guard the evolution of an image in a particular direction. Image
interfacing allowed random selection of the two parent images which were used to
breed a new population of offspring with inherited genetic traits. A system was
developed by Todd and Latham [
12
] to evolve 3D forms through geometric pro-
cedures. The system allowed Latham to test and exhaust a formal grammar of any
particular 3D form or structure (Fig.
3
).
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