42
D. Morrall
living environment. An important component of the ecosystem is the spatial
landscape within which organisms live. Naturallandscapes have complex patch
dynamics that infIuence both the distribution of organisms and their interactions
(Borhman and Likens 1979). The evolution of multiple-species within a
landscape requires representation of diverse niches with different evolutionary
pressures (Cedeno and Vemuri 1999). Organisms may move through the system
and change in number as weIl as immigrate and emmigrate as system properties
change. It is through their ability to simulate evolution in response to changing
environmental conditions and spatial heterogeneity that GAs can offer most to the
field of ecosystems ecology.
One example of a genetic algorithm that can be used to simulate evolution in
ecosystems is HoIland's Echo model (1995). Echo is a generic ecosystem model
with evolving agents and a resource limited environment. Hraber et al. (1997)
state that the primary contribution of Echo to ecological modelling is that
evolution is built in as a fundamental part of the system. Echo includes both
ecological interactions and evolutionary dynamics. This provides the potential far
evolution of ecological structure and function in response to changing conditions.
Through this approach they can simultaneously evolve multiple species and
species interactions. They consider Echo a mechanistic model because primitive
components and mechanisms are built into the model that spontaneously give rise
to macro-Ievel properties. Echo incorporates spatial attributes of the ecosystem
and the opportunity for co-evolution; both of wh ich are essential for simulation of
complex ecosystems. Agents, or individuals within Echo, occupy sites within a
two-dimensional world. They reproduce and exchange genes when they have
acquired sufficient resources through trade and combat. Each agent contains 6
external tags (offense, defense, and mating) and internal conditions (combat, trade,
and mating) genes (Figure 3.4). Internal tags are not visible to other agents.
Agents interact based on their own internal conditions and the other agents'
external tags. They have different abilities to accept and accumulate resources.
Like Echo, EUZONE (Downing 1997) describes an evolutionary computation
model that includes both ecological and evolutionary interactions. EUZONE
evolves species of phytoplankton-like creatures in a two-dimensional world. Echo
and EUZONE are designed to capture the fundamental attributes of complex
adaptive systems.
GAs can also be used to develop self-designing ecosystems. The development
of self-designing adaptive systems solves the problem that process models have in
trying to represent dynamically changing systems. Fontaine (1981) made an early
attempt at creating a self-designing ecosystem using a standard process model and
adjusting the parameters. The model parameters were optimized by running the
model for 3 time-steps, altering the parameters and rerunning the model with the
best set of parameters. The goal of this effort was "the search for a single
principle that adequately describes the evolution of ecosystem structure and
function at all levels of resolution ... " (Fontaine 1981). With evolutionary
programming not only can the parameters be optimized more effectively, but the
model structure, state values, and behaviors can be changed to produce a dynamic,
evolving ecosystem. GAs and GPs were developed to create self-designing
computer programs (Koza 1992) and are therefore the logical techniques to be
D. Morrall
living environment. An important component of the ecosystem is the spatial
landscape within which organisms live. Naturallandscapes have complex patch
dynamics that infIuence both the distribution of organisms and their interactions
(Borhman and Likens 1979). The evolution of multiple-species within a
landscape requires representation of diverse niches with different evolutionary
pressures (Cedeno and Vemuri 1999). Organisms may move through the system
and change in number as weIl as immigrate and emmigrate as system properties
change. It is through their ability to simulate evolution in response to changing
environmental conditions and spatial heterogeneity that GAs can offer most to the
field of ecosystems ecology.
One example of a genetic algorithm that can be used to simulate evolution in
ecosystems is HoIland's Echo model (1995). Echo is a generic ecosystem model
with evolving agents and a resource limited environment. Hraber et al. (1997)
state that the primary contribution of Echo to ecological modelling is that
evolution is built in as a fundamental part of the system. Echo includes both
ecological interactions and evolutionary dynamics. This provides the potential far
evolution of ecological structure and function in response to changing conditions.
Through this approach they can simultaneously evolve multiple species and
species interactions. They consider Echo a mechanistic model because primitive
components and mechanisms are built into the model that spontaneously give rise
to macro-Ievel properties. Echo incorporates spatial attributes of the ecosystem
and the opportunity for co-evolution; both of wh ich are essential for simulation of
complex ecosystems. Agents, or individuals within Echo, occupy sites within a
two-dimensional world. They reproduce and exchange genes when they have
acquired sufficient resources through trade and combat. Each agent contains 6
external tags (offense, defense, and mating) and internal conditions (combat, trade,
and mating) genes (Figure 3.4). Internal tags are not visible to other agents.
Agents interact based on their own internal conditions and the other agents'
external tags. They have different abilities to accept and accumulate resources.
Like Echo, EUZONE (Downing 1997) describes an evolutionary computation
model that includes both ecological and evolutionary interactions. EUZONE
evolves species of phytoplankton-like creatures in a two-dimensional world. Echo
and EUZONE are designed to capture the fundamental attributes of complex
adaptive systems.
GAs can also be used to develop self-designing ecosystems. The development
of self-designing adaptive systems solves the problem that process models have in
trying to represent dynamically changing systems. Fontaine (1981) made an early
attempt at creating a self-designing ecosystem using a standard process model and
adjusting the parameters. The model parameters were optimized by running the
model for 3 time-steps, altering the parameters and rerunning the model with the
best set of parameters. The goal of this effort was "the search for a single
principle that adequately describes the evolution of ecosystem structure and
function at all levels of resolution ... " (Fontaine 1981). With evolutionary
programming not only can the parameters be optimized more effectively, but the
model structure, state values, and behaviors can be changed to produce a dynamic,
evolving ecosystem. GAs and GPs were developed to create self-designing
computer programs (Koza 1992) and are therefore the logical techniques to be