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organisms such as water and specific kinds of food. Each organism will have spe-
cific needs as to the properties and resources it requires of its environment. A given
organism's preferred properties and resources define its ecological niche.
In typical “artificial life” systems evolutionary computing is implemented within
the context of a simulated ecosystem. In those systems adaptation to ecological
niches can increase diversity and enhance multi-objective optimisation. But beyond
simple adaptation genotypes within a species can actively construct niches to their
own advantage. McCormack and Bown have demonstrated both a drawing system
and a music system that exploit niche construction.
In the first system drawing agents move leaving marks, are stopped when they in-
tersect already existing marks, and sense the local density of already existing marks.
Each agent also has a genetic preference for a given density. Initially agents that pre-
fer low density will succeed in dividing large open sections of the canvas. Over time
some agents will create higher densities of marks, which in turn act as constructed
niches for progeny with a predisposition for high density. As a result some, but
not all, sections of the canvas become increasingly dense and provide niches for
high-density genotypes. The visual result exhibits a wide range of densities. Sim-
ilar agent-based systems without niche construction tend to create drawings with
homogeneous density. This system is further discussed in Chap. 2.
In the second system a single row of cells is connected head-to-tail as a toroid.
Each cell generates a sine wave creating a single frequency tone. A line runs through
all of the cells, and at each cell the line height is mapped into the loudness of its sine
wave. Agents inhabit the cells, and each has a genetic preference for line height
and slope. Each agent applies these preferences as pressure to the line in its cell as
well as the cell to its left. Depending on the local state of their niche, i.e. the line
height and slope in their cell, agents will stay alive and reproduce or die and not pass
on their genotype. This sets up a dynamic system with localities that benefit certain
genotypes. Those genotypes then modify the ecosystem, i.e. the line, to the benefit of
their progeny. The resulting sound exhibits a surprising diversity of dynamics even
though it is initialised at zero. As with many evolutionary and generative systems,
this is due to the random variation in the initial population of agents.
10.2.11.3 Agent Swarm Behaviour
In most of the evolutionary systems discussed so far there is no interaction between
phenotypes. Each is independently evaluated via user selection or fitness function.
Other than this comparison a given phenotype has no impact on another. When
phenotypes begin to interact in other ways, typically in the context of a simulated
ecosystem, they can be thought of as simulated organisms or
agents
that exhibit
behaviours. With niche creation agents modify their ecology establishing a mediated
form of agent interaction. But agents can also interact directly creating an emergent
group behaviour or
swarm behaviour
.
The canonical natural example of such an agent is the ant. An ant colony uses
swarm intelligence to optimise the gathering and retrieval of food. As an ant finds
