The majority of species on earth have evolved skills for locomotion in water,
and the variation in morphology among species is matched by a great variety
of swimming styles. For most species, locomotion is necessary for getting
to food and, for many, to avoid becoming someone else's food. Locomotion
also plays a role in the reproductive lives of most species. In order for
an individual to mate with another of its species, it needs to approach this
individual to be close enough - obviously - to mate. The simulation described
in this paper bases its ability to generate emergent locomotion and morphology
from these two simple elements of nature - food and sex.
This paper describes a simulation which drives an artificial life movie - a
movie with no script. The actors are a variety of 2D figures, called swimmers,
which inhabit a virtual pond. The swimmers' bodies consist of interconnected
line segments of many colors, with movable joints. The motions of body
parts can exert forces against the water and thus potentially accelerate
a swimmer through the water. In this world, swimmers eat, reproduce, and die.
Swimmers who are able to swim to food can replenish their energy.
Those who cannot, die of starvation. Swimmers who are both able to
eat and able to swim to a chosen mate, reproduce. Those who cannot
mate, do not pass on their genes to future swimmers before dying.
Evolution of improved swimming results. On the local scale
(individual swimming styles) as well as the global scale
(group population dynamics), emergent phenomena can be observed.
Sexual selection is modeled here as well - each swimmer has a genetically
inherited favorite color in potential mates, which influences which swimmer
it chooses from those within its view. These preferences are randomized
at initialization, along with all the other genes. Eventually, in maturing
populations, swimmers' color preferences begin to correspond with their
actual body colors, often catalyzing distinct groups to emerge, in which
case, the population becomes sympatric, with groups rarely interbreeding,
particularly among the "purest" races, in which body color is homogeneous.
The use of color in modeling mate preference is not for aesthetics
(not ours, at least - perhaps the swimmers'), but as a readily-visualized
way of studying the effects of mate preference.
1.1 Autonomous Reproduction
Operators derived from the genetic algorithm (GA) are used in this
simulation, but not in the conventional manner: in this case there are no
discrete generations, and there is no explicit use of a fitness function
for assigning fitness values to individuals in a population, thereby
determining the rate at which they can reproduce. Instead, creatures
who are able to swim towards a desired mate automatically reproduce,
by virtue of the fact that they are able to swim to their goal.
The fitness landscape is continually changing, as in the natural world.
Fitness, then, is equivalent to reproduction, which, in this world,
is equivalent to coming in contact with a desired mate.
At no time is any "outside assistance" used in the simulation
to help encourage the emergence of optimized swimming - other than
in the initial distribution of swimmers and food, and a set of swimmer
perception and behavior settings. There is nothing but the situation
at hand to determine how the population will evolve. In many simulation
runs, the entire population of swimmers dies off before any significant
results occur. This is the occasional price to pay for a decidedly hands
This approach also offers no means of exploiting a good solution
when it happens somewhere in the pond. An Olympic individual
can emerge within the pond, but if it has an inappropriate preference
for mates, is not desired by other mates, or is born in a low swimmer
or food populated area, it could die without ever having reproduced.
This simulation runs on a mixture of skill and luck.
This work can be seen as an attempt to extend recent methods
in generating emergent morphology and motor control in virtual creatures
using GA's. In this simulation, the changing environmental conditions
(and not explicitly a "Creator's" objective function) determine emerging
behaviors, which are transformed by (and likewise transform)
the environment. The emphasis, then, is not on simply optimizing
behavior within a population, but in modeling and studying the
heterogeneous outcome of a population of creatures living their
lives within an ever-changing, varying ecosystem.
1.2 Real-time Animation
The simulation described here has an interactive component - the design of
which has been a useful investment for development and analysis. One can view
the running of a simulation using an interactive "microscope" which can zoom
in and out, or pan across the pond. With this microscope, different parts of
the simulation can be studied - one can watch a single swimmer up close; view a
genetically related group of swimmers in one area of the pond; or view the
entire pond to witness large-scale dynamics.
Populations of about 200 or less can be watched at real-time computer
animation rates (10 or more frames per second) on an SGI Indigo2 computer.
In experiments, the population count is usually initialized to 1000 - which
is too high for real-time animation: it is not yet visually informative.
But the population inevitably drops way below this number as the ecosystem
stabilizes, leaving a smaller number of statistically better swimmers.
After this, informative and enjoyable animations can be watched for hours.