Computational Genetics, Physiology, Metabolism,
Neural Systems, Learning, Vision, and Behavior
or PolyWorld:  Life in a New Context

Larry Yaeger
Apple Computer, Inc.
20525 Mariani Ave., MS 76-2H
Cupertino, CA  95014
larryy@apple.com


1. Introduction

	The study of living systems has taken many forms, from 
research into the fundamental physical processes to ethological 
studies of animal behavior on a global scale.  Traditionally these 
investigations have focused exclusively on ÒrealÓ biological systems 
existing in our worldÕs ecological system.  Only recently have 
investigations of living systems begun to occur in ÒartificialÓ systems 
in computers and robotic hardware.

	The potential benefits of an enhanced understanding of living 
systems are tremendous.  Some are of a grand scale, and are 
intuitively obvious, such as improvements in our ability to manage 
our own real ecosystems, the development of true machine 
intelligence, and the possibility of understanding our own mental and 
physiological processes.  Some are of a more prosaic scale, but more 
accessible thereby, and, perhaps, of more immediate utility, such as 
simple learning systems, robust pattern classifiers, general purpose 
optimization schemes, robotic controllers, and evolvable software 
algorithms.  The technological issues of the study of Artificial Life 
(ALife) are well laid out by Langton [27] in the proceedings of the 
first ALife workshop; the societal and philosophical implications of 
ALife are well presented in Farmer and Belin [16] in the proceedings 
of the second ALife workshop.

	The ecological simulator, PolyWorld (PW), presented and 
discussed in this paper, is one instantiation of these ALife 
motivations and principles.  PolyWorld attempts to bring together all 
the principle components of real living systems into a single artificial 
living system.  PolyWorld does bring together biologically motivated 
genetics, simple simulated physiologies and metabolisms, Hebbian 
learning in arbitrary neural network architectures, a visual 
perceptive mechanism, and a suite of primitive behaviors in artificial 
organisms grounded in an ecology that is hopefully just complex 
enough to foster speciation and inter-species competition.  Predation, 
mimicry, sexual reproduction, and even communication are all 
supported in a straightforward fashion.  The resulting survival 
strategies, both individual and group, are purely emergent, as are the 
functionalities embodied in their neural network ÒbrainsÓ.  Complex 
behaviors resulting from the simulated neural activity are 
unpredictable, and change as natural selection acts over multiple 
generations.

	PolyWorld is a tool for investigating issues relevant to 
evolutionary biology, behavioral ecology, ethology, neural systems, 
and computer science.  This paper will discuss the design principles 
employed in PW, along with some of the resulting behavior patterns 
observed in ÒspeciesÓ evolved in PW, their neural architectures, and 
the genetic variations observed in large populations under different 
ecological conditions.



2. Background

	This work owes much in terms of inspiration to the work of W. 
Grey Walter [44,45,46], Valentino Braitenberg [3], Richard Dawkins 
[9,10,11], John Holland [21], Ralph Linsker [28,29,30], and John 
Pearson [36].

	WalterÕs early work with simple electronic ÒturtleÓ nervous 
systems, and BraitenbergÕs ÒvehiclesÓ suggested the approach of 
utilizing simulated organisms.  PolyWorld diverges from these works 
by encapsulating these organisms in a simulated world, and 
employing neural systems and learning rules from the world of 
computational neurophysiology, and by supporting a range of 
interactions between organisms.  And though a number of other 
researchers (Travers [43]; Wharton and Koball [47]; and even a 
commercial product from Bascom software, the author of which is not 
known) have built simple Braitenberg vehicle simulators (or actual 
physical models in the case of Wharton and Koball), these typically 
concentrated on a wiring-diagram user interface, and implemented 
vehicles through only level number 2 (of 14).  PW, on the other hand, 
takes note of the fact that by as early as Vehicle 4, Braitenberg 
invoked a form of natural selection, and supports the evolution of its 
organismsÕ Òwiring diagramsÓ, rather than having them specified by 
hand.  The neural systems of PW also utilize Hebbian learning during 
the lifetime of an individual, which is undoubtedly purposefully 
similar to BraitenbergÕs Òmnemotrix wireÓ.

	Richard DawkinsÕs writings communicate both the beauty and 
the effectiveness of evolutionary dynamics.  In personal 
communications, he has also brought out key issues in speciation, 
such as the isolation of populations and the reduced viability of 
divergent species interbreeding, that have become important 
elements of this simulator.

	The artificial neural systems employed in PW are based on 
Hebbian learning, and a novel approach to network architecture 
specification.  Besides the obvious importance of Donald HebbÕs [18] 
research and speculations, their instantiation in the work of Ralph 
Linsker and John Pearson has guided the selection of these particular 
techniques for use in PW.  LinskerÕs work demonstrated that Hebbian 
learning, as employed in PW, can and will self-organize important 
types of neural response patterns observed in early visual systems 
of real organisms.  John Pearson, working with Gerald Edelman, 
utilized a variant of Hebbian learning and successfully demonstrated 
important principles of neuronal and synaptic self-organization Ñ 
cooperation and competition(for representing their observed inputs) 
Ñ that again correspond well to phenomena observed in real living 
systems.  PolyWorld takes this unsupervised learning technique, and 
embeds it in arbitrary, evolving neural architectures, and then 
confronts the simulated neural system with survival tasks in a 
simulated ecology.

	In the last couple of decades, a number of researchers have 
developed computational ecologies targeted at various scientific 
issues.   Conrad [7,8] and Packard [34] have built systems to explore 
fundamental principles of evolutionary dynamics.  Jefferson et al 
[23],  and Collins and Jefferson [6] have constructed systems dealing 
with evolutionary mechanisms, behavioral goals, and learning 
architectures (Finite State Automata vs. Neural Networks).  Taylor et 
al [40] developed a system to investigate the relationship between 
individual behavior and population dynamics.  Ackley & Littman [1] 
built such a simulator to demonstrate a novel mechanism by which 
evolution can guide learning.  Peter Todd and Geoffrey Miller 
[31,41,42] have explored evolutionary selection for different learning 
algorithms in organisms with simple vision systems and an innate 
sense of ÒsmellÓ that functions with varying degrees of accuracy.  
Danny Hillis [19,20] has used simple computational ecologies to 
evolve ÒrampsÓ, and exchange-sort algorithms.  Core Wars [13,14,15]  
is a non-evolving ecology of code fragments, and RasmussenÕs VENUS 
[37] is an evolving system based largely on Core Wars.  Thomas Ray 
[38] has also developed a computational ecology, TIERRA, based on 
evolving code fragments.  And John Koza [26] has developed a 
system for evolving LISP functions that he terms ÒGenetic 
ProgrammingÓ.  PolyWorld, in its original conception, was targeted 
principally at the evolution of neural architectures for systems faced 
with complex behavioral tasks;  however, its biologically motivated 
behavioral and reproductive strategies, and the evolutionary 
mechanisms employed also make it suitable for use in behavioral 
ecology and evolutionary biology.  The extent of PWÕs fidelity to 
biological systems, together with its unique use of a naturalistic 
visual perceptive system to ground its inhabitants in their 
environment distinguish it significantly from previous ecological 
simulators.

	John HollandÕs ECHO system explicitly models a form of 
predation, involving ÒoffenseÓ and ÒdefenseÓ genes that determine 
the outcome of violent encounters.  Holland notes that in his system, 
this form of predation was essential to the evolution of complex 
genomes.  Though not as crucial to PWÕs genetic complexity, 
predation was also designed into PW from the beginning.  In PW, 
genes also affect the outcome of violent encounters between 
organisms, but more indirectly through their ÒphysiologicalÓ 
characteristics (strength and size).  There is also a behavioral 
component to the outcome of these encounters in PW, namely the 
degree of ÒvolitionÓ associated with the ÒfightingÓ behavior (the 
activation level of a predefined ÒfightÓ neuron), that differs from 
ECHOÕs handling of predation.

	Belew et al [2] give an excellent overview of recent work in the 
area of evolving neural networks.  Reviewed briefly there, and 
presented in detail in their own paper, Harp et al [17] have 
developed a scheme for evolving neural architectures that has an 
element of ontogenetic development.  Their approach involves a set 
of synaptic projection radii between neuronal ÒareasÓ.  PolyWorldÕs 
scheme for evolving architectures relies on the specification of 
connection densities and topological distortion of connections 
between neuronal groups.  These architectural criteria are 
represented in the genome, and then expressed as an organismÕs 
neural architecture at ÒbirthÓ.  This technique, though perhaps not 
quite as developmental as HarpÕs approach, or the non-neural, but 
very biologically motivated cellular growth work of de Boer et al 
[12], has the strengths of being much more developmental  (and 
representationally compact) than a simple encoding of synaptic 
efficacy in the genes, and being computationally very efficient.  It 
captures the statistical results of development, without the necessity 
of modeling the developmental process itself.

	David Chalmers [4]  has experimented with evolving supervised 
neural network learning algorithms, successfully evolving the classic 
"delta rule" for a linear, single layer perceptron, and speculated on 
applying this "genetic connectionism" approach to other architectures 
and learning algorithms.  He also varies the diversity of his learning 
tasks, and demonstrates a correlation between this diversity and the 
generality of the evolved learning algorithm, similar to the 
correlation observed between amount of training data and 
generalization in supervised, "Back-Prop" neural networks.  Though 
the evolution of unsupervised learning algorithms is one area of 
special interest to the author, the current version of PW has the 
classic "Hebb rule" built in.  Neural architectures are, however, 
evolved in PW.  Interestingly, by permitting free movement in a 
simulated environment, PW effectively can generate an unlimited 
amount of diverse input for the neural mechanisms employed by its 
denizens.

	Nolfi et al [33] and Parisi et al [35] have explored evolving the 
connection strengths in small, fixed-architecture feed-forward neural 
networks controlling simple movement strategies in organisms 
evolved to seek food.  The organisms are directly provided with 
angle and distance to food items, and are alone in their environment.  
Nolfi, Parisi, et al also introduce a "self-supervised" learning 
technique, using the traditional back-propagation of error algorithm, 
and demonstrate an improvement in evolved foraging efficiency 
associated with a learned ability to predict the sensory consequences 
of motor activity.  PW employs an unsupervised learning algorithm 
and arbitrary neural architectures, with a more biologically-
motivated vision mechanism, as well as a competitive ecology.

	For the purpose of computer graphics animation, Renault et al 
[39] have experimented with visual systems for controlling computer 
generated characters.  Their system goes beyond visual processing, 
however, to include unique object identification and distances to 
objects as part of the input to the character control programs.  These 
control programs are rule-based and completely hand-crafted, 
specifically to provide obstacle avoidance.  In contrast, PW uses only 
the pixel colors associated with visual processing, and provides these 
as input to the non-rule-based neural systems of evolving organisms, 
without specifying the meaning or use of this information.  

	Dave Cliff [5] has implemented a neural visual system for a 
simulated fly, and states that it is only by a grounding perceptive 
mechanism such as vision that neural models can be made sense of.  
For the purposes of his simulation, the model fly is attached to a 
rotating, but otherwise unmoving test-stand similar to real 
experimental setups.  Organisms in PW use vision as their primary 
sense mechanism, but are free to explore their environment, and 
must do so effectively Ñ using their vision to guide a suite of 
primitive behaviors Ñ in order to survive and reproduce.

	The study of real living systems has spanned many physical 
and temporal scales,  from molecular level biochemical processes that 
take place in nanoseconds, through cellular level neural processes 
with time scales of a few milliseconds, to global evolutionary 
processes occurring over geological time scales.  One of the first 
decisions necessary to commence an investigation into artificial living 
systems is that of scale:  At what level of detail is it desirable to 
specify the parameters and underlying models of the simulation, and 
at what level does one wish to observe the resultant behaviors?  
Given current constraints on compute power, it is simply not feasible 
to begin computation with sub-atomic physics and expect to observe 
ethological behaviors.  Since ecology-level dynamics were the desired 
output level of the system being designed, it was clear that behavior 
models for PWÕs individual organisms could not be too complex.  
However, a desire to avoid rule-based behavior specification led to a 
decision to model the organismsÕ behaviors at the neuronal level.  
Since even natural evolutionary forces are constrained by their 
previous successes, the real world has filled up with organisms 
exhibiting a wide range of variations on assemblages of neuronal 
cells (in addition to other cell types, of course).  Modeling PWÕs 
organisms at this level permits us to sidestep millions of years of 
evolution, while still taking advantage of its results to date.  In many 
ways, PW may be thought of as a sort of electronic primordial soup 
experiment, in the vein of Urey and MillerÕs [32] classic experiment, 
only commencing at a much higher level of organization, with light-
sensitive and neuronal cells as the ingredients, and a simple ecology 
that includes all the other assemblages of those cells Ñ the other 
organisms in the world Ñ as the environment.


3. Overview

	PolyWorld is an ecological simulator, of a simple flat world, 
possibly divided up by a few impassable barriers, and inhabited by a 
variety of organisms and freely growing ÒfoodÓ.  The inhabiting 
organisms use vision as input to a neural network brain that employs 
Hebbian learning at its synapses.  The outputs of this brain fully 
determine the organismsÕ behaviors.  These organisms and all other 
visible constituents of the world are represented by simple polygonal 
shapes.  Vision is provided by rendering an image of the world from 
each organismÕs point of view, and using the resulting pixel map as 
input to the organismÕs brain, as if it were light falling on a retina.

	A small number of an organismÕs neurons are predetermined to 
activate a suite of possible primitive behaviors, including eating, 
mating, fighting, moving forward, turning, controlling their field of 
view, and controlling the brightness of a few of the polygons on their 
bodies.  Organisms expend energy with each action, including neural 
activity.  They must replenish this energy in order to survive.  They 
may do so by eating the food that grows around the environment.  
When an organism dies, its carcass turns into food.  Because one of 
the possible primitive behaviors is fighting, organisms can 
potentially damage other organisms.  So they may also replenish 
their energies by killing and eating each other.  Predation is thus 
modeled quite naturally.

	The organismsÕ simulated physiologies and metabolic rates are 
determined from an underlying genome, as are their neural 
architectures.  When two spatially overlapping organisms both 
express their mating behavior, reproduction occurs by taking the 
genetic material from the two haploid individuals, subjecting it to 
crossover and mutation, and then expressing the new genome as a 
child organism.

	One way to look at this artificial world is as a somewhat 
complex energy balancing problem.  The fittest organism will be the 
one that best learns to replenish its energies by eating, and to pass 
on its genes by mating.  The particular patterns of activity that a 
successful organism engages in - the methods by which it sustains 
and reproduces itself - will be optimal for some particular fitness 
landscape.  But since that fitness landscape depends upon the 
behavior of the world's other inhabitants, it must, per force, be a 
dynamic landscape.  Since there is considerable variation in the 
placement and behavior of food and other organisms in the world, 
that fitness landscape is also fundamentally stochastic.  Indeed, if the 
"fittest organism in the world" fails to find a suitable mate in order to 
pass on the important bits of its genetic material, then those genes 
will be lost... possibly for all time.  Accordingly, every world has the 
potential to be quite different from every other world.

	Once an Evolutionarily Stable Strategy (ESS) has emerged, there 
is no fitness function except survival.  Until an ESS has emerged, PW 
is run in a sort of Òon-line Genetic Algorithm (GA)Ó mode, with an ad 
hoc fitness function.  During this stage, a minimum number of 
organisms may be guaranteed to populate the world.  If the number 
of deaths causes the number of organisms extant in the world to 
drop below this minimum, either another random organism may be 
created by the system, or the offspring of two organisms from a table 
of the N fittest may be created, or, rarely, the best organism ever 
may be returned to the world unchanged.  This ad hoc fitness 
function rewards organisms for eating, mating, living their full 
lifespan, dying with reserve energies, and simply moving.  Each 
reward category is normalized by the maximum possible reward in 
each category, and has a specifiable scale factor to permit easy 
tuning of the fitness function.  Some simulation runs acquire an ESS 
in the first seed population and never require this on-line GA stage.  
Throughout this paper, the term created is applied to organisms 
spontaneously generated by the system, while born is used to refer 
to organisms resulting from the mating behaviors of the organisms.

	Current high end simulations typically involve over 300 
organisms, with up to approximately 200 neurons each, and require 
about 13 seconds per time-step on a Silicon Graphics Iris 4D/240-
GTX.  With an average lifespan of about 500 time-steps, and a time-
to-first-offspring of about 100 time-steps, this means that 500 
generations can be run at this complexity in about 1 week.  More 
modest simulations with around 100 comparable organisms require 
about 4 seconds per frame, and take a day or two for the same task.  
And at the low complexity end, simple demonstration worlds can be 
run in Òreal timeÓ, at a few frames per second, and allow a more 
interactive experience for learning the system.

	Figure 1 shows a sample view of the PolyWorld terrain, 
populated with three related but distinct sub-species.  The largest 
panel shows a broad view of the world:  the dark green ground 
plane, the brown, impassable barriers, the bright green pieces of 
food, and the multicolored organisms.  Just above this oblique world 
view are four graphs of various informative simulation parameters.  
Above these at the top of the figure are many small views of the 
world drawn from the point of view of each of the organisms in the 
world; these are the images seen by the those organisms.  At the top 
right are a few numerical statistics.  And in the bottom right pane is 
a close-up view of the current ÒfittestÓ organism.


4. Genetics

	An organismÕs genes completely encode both its ÒphysiologyÓ 
and its neural architecture.  Table 1 lists the full complement of 
genes present in the organisms of PW.

	¥ size
	¥ strength
	¥ maximum speed
	¥ ID
	¥ mutation rate
	¥ number of crossover points
	¥ lifespan
	¥ fraction of energy to offspring
	¥ number of neurons devoted to red component of vision
	¥ number of neurons devoted to green component of vision
	¥ number of neurons devoted to blue component of vision
	¥ number of internal neuronal groups
	¥ number of excitatory neurons in each internal neuronal 
group
	¥ number of inhibitory neurons in each internal neuronal 
group
	¥ initial bias of neurons in each non-input neuronal group
	¥ bias learning rate for each non-input neuronal group
	¥ connection density between all pairs of neuronal groups and 
neuron types
	¥ topological distortion between all pairs of neuronal groups 
and neuron types
	¥ learning rate between all pairs of neuronal groups and 
neuron types

Table 1.  List of genes in organisms of PolyWorld.


	All genes are 8 bits in length, and may be Gray-coded or 
binary-coded.  All but the ID gene are used to provide 8 bits of 
precision between a specifiable minimum and maximum value for 
the corresponding attribute.  For example, if the minimum possible 
size is minSize, and the maximum possible size is maxSize, and the 
value of the size gene (scaled by 255 to lie between 0.0 and 1.0) is 
valSizeGene, then the size of the organism with this gene will be:

	size = minSize + valSizeGene * (maxSize - minSize)

These extrema values, along with a variety of other controlling 
parameters for the simulation, are contained in a Òworld fileÓ that is 
read by the simulator at startup.

	The first 8 genes control the organismÕs simulated physiology.  
Its size and strength affect both the rate at which it expends energy 
and the outcome of ÒfightsÓ with other organisms.  In addition, its 
size is related directly to the maximum energy that it can store 
internally.  The next gene, maximum speed, also affects its 
ÒmetabolicÓ rate.

	The ID geneÕs only function is to provide the green component 
of the organismÕs coloration at display time.  Since organisms can 
actually see each other, this could, in principle, support mimicry. For 
example, a completely passive species could evolve to display the 
green coloration of a very aggressive species if it were of selective 
advantage.  It might also be possible to attract potential mates by 
displaying the green coloration of food, though this might be of 
limited survival value.  (In practice, however, neither of these 
somewhat sophisticated evolutionary responses has yet been 
observed.)

	Mutation rate, the number of crossover points used during 
reproduction, and maximum lifespan were placed in the genes in 
order to permit a kind of meta-level genetics, and in recognition of 
the fact that these parameters were themselves evolved in natural 
systems.  They are, however, typically constrained to operate within 
ÒreasonableÓ limits; 0.01 to 0.1 for mutation rate, 2 to 8 for number 
of crossover points, and a few hundred to a few thousand Òtime-
stepsÓ for lifespan.

	The final physiology gene controls the fraction of an organismÕs 
remaining energy that it will donate to its offspring upon birth.  The 
offspringÕs total available energy on birth is the sum of these 
contributions from the two parents.  Accordingly, at least one aspect 
of sexual reproduction may be captured by PWÕs evolutionary 
ÒbiologyÓ:  it is entirely possible for two interbreeding sub-species to 
be almost identical genetically, differing only in the amount of 
personal energy devoted to the reproductive process.  PolyWorld has 
not yet been instrumented to observe for this phenomenon.

	The remaining genes are used to define the organismÕs neural 
architecture.  These control parameters will be discussed in the 
section on Neurons and Learning.  It should be noted here, however, 
that one of the motivations for this method of specifying the neural 
architecture was to reduce the number of genes necessary to specify 
the neural system.  Early versions of PW used a simpler, fully 
recurrent neural architecture, and maintained a complete matrix of 
synaptic efficacies between all pairs of neurons in the genes.  For 200 
(NN) neurons, this older model required 40,000 (NN2) genes.  The 
current scheme supports evolving neural architectures which are 
fully specified by 12NG2 + 232NG + 1026, where NG is the number of 
internal neuronal ÒgroupsÓ or clusters (output group sizes are fixed to 
1, and input groups do not need biases, bias learning rates, or 
incoming synaptic connections).  Thus, for 4  internal groups, with up 
to 32 neurons per group, plus up to 16 neurons per vision group (of 
which there are 3, one for each color component:  red, green, blue), 
plus 2 other input groups (one neuron per group), plus the standard 
7 output groups (one neuron each), a network of up to 185 neurons 
can be fully specified by just 2,146 genes.  The large constants in this 
equation (232 and 1026) are due to the fixed set of input and output 
groups, and, especially, the desire to maintain each output neuron as 
a distinct group.  Though the number of specifications are 
significantly reduced from a full crossbar matrix, this number still 
heavily outweighs the number of genes devoted to physiology.  To 
permit a more robust exploration of the space of possible 
physiologies, then, the first crossover during genetic reproduction is 
always forced to occur somewhere within the set of physiology 
genes.

	An organismÕs genome is allocated and interpreted such that 
space is available for the maximum possible number of neuronal 
groups.  That is, one of the parameters specified per pair of groups in 
a network with 3 groups out of a maximum of 5 groups would be 
accessed as:

	1,1     1,2     1,3     --     --     2,1     2,2     2,3     --     --     --     
3,1     3,2     3,3     --     --

where the entries marked Ò--Ó serve simply as place holders.  This is 
as opposed to an access scheme looking like:

	1,1     1,2     1,3     2,1     2,2     2,3     3,1     3,2     3,3

where the entries are contiguous.  The reason for this is to permit a 
smoother evolution of these neural architectures.  The addition of a 
fourth group would leave the old connections intact in the first 
representation, but not in the second.  It is even possible for a useful 
subcomponent of the architecture to ride along dormant in the genes 
to be expressed at a later time.

	Though learning is supported in the neural network model 
employed in PW, only the architecture and some initial values are 
encoded in the genes; hence evolution in PW is purely Darwinian, not 
Lamarckian.

	As in most GAÕs, when an organism is created  from scratch, the 
bits in its genes are first zeroed, and then turned on with a certain 
bit probability.  Unlike most GAÕs, it is possible to specify a range of 
legal bit probabilities, rather than always using 0.5.  The bit 
probability for an individual organism is then randomly selected 
from this range and used to initialize the organismÕs bit-string 
genome.   So the probability of a bit being on in a particular organism 
will depend on the value randomly selected from the specified range, 
while the probability of a bit being on in the population as a whole 
will just be the mean of the specified range (0.5 if the range is 0.0 to 
1.0).  This permits a wider variance in local, organism-specific bit 
probabilities in early populations, rather than depending entirely on 
mutation and cross-over to so shuffle the bits.  Whether this is of any 
real value should be tested in a simpler GA system, and may be 
problem-specific in any event.  Here it was felt that both the older 
fully-recurrent neural network architecture and the later evolving 
neural architectures were more likely to have 
behaviorally/evolutionarily useful solutions with lower bit densities; 
this provided a mechanism for so biasing the initial seed population 
without ruling out selection towards the unexpected end of the 
spectrum.

	There is an optional Òmiscegenation functionÓ (so dubbed by 
Richard Dawkins), that may be used to probabilistically influence the 
likelihood of genetically dissimilar organisms producing viable 
offspring; the greater the dissimilarity, the lower the probability of 
their successfully reproducing.  This function is not typically invoked 
until after a (specifiable) ÒsignificantÓ number of births without an 
intervening creation in order to allow the early stages of the 
simulation to explore as many genetic recombinations as possible.


5. Physiology and Metabolism

	As discussed above, the simulated physiology of PolyWorldÕs 
organisms is determined by their genes.  The size of the organism 
directly affects the maximum amount of energy that the organism 
can store.  If an organismÕs size is allowed to range between minSize 
and maxSize, and its energy capacity ranges between minECap and 
maxECap, then a given organismÕs actual energy capacity, ECap is 
given as:

	ECap = minECap + (size - minSize) * (maxECap - minECap) / 
(maxSize - minSize)

Similar linear relations are used to determine the influence of an 
organismÕs size on the rate at which it expends energy during 
forward or turning movement (relative to a specifiable maximum-
size-penalty), and a size-advantage it will have during a fight with 
another organism (relative to a specifiable maximum-size-
advantage).

	An organismÕs strength also affects both its energy expenditure 
and its advantage in a fight.  Strength directly scales the total energy 
used in a given time step, and thus usually ranges around 1.0 
(typically 0.5 to 2.0).  An attackerÕs strength also scales the effect on 
the victimÕs energy loss (fighting is discussed in more detail below in 
the section on Behavior).

	The energy expended by an organismÕs neural processing is 
determined linearly from the number of neurons and the number of 
synapses it has.  A maximum number of neurons and synapses is 
determined from the control parameters for the entire world, then 
each individualÕs neural energy expenditure is computed relative to 
these maxima.  Globally applied Òneuron-to-energyÓ and Òsynapse-to-
energyÓ conversion factors then multiply these scaled neuron and 
synapse counts to determine the actual energy expended per time 
step.

	There are similar behavior-to-energy conversion factors for 
each of the primitive behaviors (eating, mating, fighting, moving, 
turning, focusing, and lighting).  The total energy expended in a time 
step is then the activation (0. to 1.) of the corresponding 
output/behavior neuron multiplied by that behaviorÕs energy-
conversion factor, summed over all behaviors, plus the neural energy 
expenditure, plus a specifiable fixed energy drain, with this sum 
finally scaled by the organismÕs strength.

	As should be evident, there are clear energy conservation 
benefits to being small and weak, yet there are clear predatory 
advantages to being large and strong.  Size also permits an overall 
greater capacity to store energy, thus making energy available for 
additional behavioral activity, including reproduction.  The interplay 
between these opposing advantages is intended to produce niches in 
the fitness landscape, which may change over time.  There are 
similar opposing pressures between energy expenditure and visual 
acuity on the number of input neurons devoted to vision.

	There are two classes of energy storage in each organism:  
health-energy, and food-value-energy.  Both are replenished by 
eating food.  Both are depleted by neural activity and by engaging in 
the various behaviors.  But when an organism is attacked, only its 
health-energy is depleted by the attack.  If this health-energy 
reaches zero, the organism dies.  When an organism dies it is 
converted into a piece of food containing an amount of energy equal 
to the organismÕs food-value-energy.  This separation of health-
energy from food-value-energy makes the predator-prey 
interactions quite natural; i.e., it is possible for an organism to be 
killed by having its health-energy driven to zero, while still 
maintaining a relatively high food value for the attacker.

	An organismÕs food-value-energy will always be greater than 
or equal to its health-energy, yet both classes of energy have the 
same maximum capacity.  Accordingly, an organism may continue to 
eat to replenish its health-energy after its food-value-energy has 
reached capacity.  It is the health-energy that is provided as input to 
the neural network (see next section), and that is used to determine 
the amount of energy to be transferred to offspring.

	Purely for the purposes of display, an organismÕs length and 
width are scaled by (the square root of) its maximum speed, length 
being multiplied, width being divided.  Thus faster individuals will 
appear longer and sleeker, while slower individuals will appear 
shorter and bulkier.  Since an organismÕs visual acuity is subject to 
evolutionary pressures, it is conceivable that an organism might 
emerge that was able to ascertain another organismÕs maximum 
speed purely from its shape, if there was a great enough advantage 
to the acquisition of this information (though this is not expected to 
be the case).


6. Neural Systems and Learning

	The inputs to an organismÕs neural network ÒbrainÓ are its 
ÒvisionÓ, the current normalized level of its internal health-energy 
store, and a random value.  The outputs are the suite of 7 possible 
primitive behaviors (eating, mating, fighting, moving, turning, 
focusing, and lighting).  The internal neurons and all of the synaptic 
connections have no prespecified functionality; their utility is 
determined entirely by genetics and natural selection.

	The form of an organismÕs brain, or neural system, is fully 
characterized by a set of parameters that are encoded in its genes.  
Referring back to Table 1, notice that the number of neurons devoted 
to each color component of vision is specified separately, permitting 
a specialization for more resolution in the most effective color, should 
this be of selective advantage.  These numbers typically range 
between 1 and 16 neurons per color.

	Next is a parameter that specifies the number of internal 
neuronal groups or clusters.  This typically ranges from 1 to 5.  In 
addition, there are 5 input groups (red vision, green vision, blue 
vision, energy level, and random), plus 7 output groups (the 
behaviors listed above).

	Each neural group may have distinct populations of excitatory 
(e-) and inhibitory  (i-) neurons.  The number of e- and i-neurons 
are specified on a per group basis, and typically range between 1 and 
16 neurons of each type.  Synaptic connections from e-neurons are 
always excitatory (ranging from 0.0 to a specifiable maximum 
efficacy).  Synaptic connections from i-neurons are always inhibitory 
(ranging from -1.e-10 to the negative of the maximum efficacy).

	Though the bias on each of the non-input neurons varies 
during the simulation, the initial values for these biases and their 
learning rates are specified on a per group basis, for each of the non-
input neural groups.  Biases are updated by a Hebbian learning rule, 
as if it were a synaptic connection to a neuron that was always fully 
activated, but unlike other synapses in this network, the bias may 
change sign.  Biases typically range from -1.0 to 1.0, and bias 
learning rates typically range from 0.0 to 0.2.

	The remaining parameters - connection density (CD) , 
topological distortion (TD), and learning rate (LR) - are all specified 
for each pair of neuronal groups and neuron types.  That is, separate 
values for each of these parameters are specified for the excitatory-
to-excitatory (e-e), excitatory-to-inhibitory (e-i), inhibitory-to-
inhibitory (i-i), and inhibitory-to-excitatory (i-e) synaptic 
connections between group i and group j, for each pair of groups i 
and j.

	Connection density, as the name suggests, is used to determine 
the extent of the connectivity between neuronal groups.  The number 
of e-e synapses between group i and group j is given by the nearest 
integer to  CDe-e(i,j) * Ne(i) * Ne(j), where      CDe-e(i,j) is the e-e CD 
from group j to group i, Ne(i) is the number of e-neurons in group i, 
and Ne(j) is the number of e-neurons in group j.  Similar expressions 
hold for the other types of connections between all pairs of groups.  
CD can range from 0.0 to 1.0.

	Topological distortion is used to determine the degree of 
disorder in the mapping of synaptic connections from one group to 
the next.  That is, for a TD of 0.0, synapses are mapped to perfectly 
contiguous stretches of neurons in the adjacent layer; for a TD of 1.0, 
synapses are mapped in a completely random fashion between 
adjacent layers.  Thus retinatopic maps such as are observed in 
natural organisms can be enforced (or not) at the architectural level 
(as well as resulting from the learning process).  TD typically ranges 
from 0.0 to 1.0.

	Learning rate controls the Hebbian learning process at the 
synapses between each pair of neuronal groups.  This permits 
natural selection to favor hardwired, ÒinstinctiveÓ connections for 
some neural pathways, while supporting learning in other pathways.  
LR typically ranges from 0.0 to 0.2.

	This method of specifying the neural architecture is fairly 
general, and is not biased for any particular neural organization.  
Possibly, one might expect to evolve a preponderance of inhibitory 
connections, especially locally, if the simulated neural architectures 
evolve to match real neural systems; yet the possibility exists for 
establishing local excitatory connections (such as are found in CA3 in 
the hippocampus).   The technique does not, however, explicitly 
model architectures whose characteristics are heavily based upon 
spatial organization (such as the parallel fibers originating from the 
granule cells in the cerebellum).  A straightforward extension to the 
current method, that allowed unique specifications of the same 
parameters along multiple spatial dimensions, could account for such 
organizational schemes.  However, with the limited compute 
resources currently being applied to PW simulations, and thus the 
limited number of neurons permitted in each brain, it was not 
deemed worthwhile to further decompose the groups into these 
spatial subcategories.

	When an organismÕs brain is ÒgrownÓ from its underlying 
genome, the synaptic efficacies are randomly distributed between 
specifiable minimum and maximum values.  The brain is then 
exposed to a sequence of mock visual inputs consisting of random 
noise, for a specifiable number of cycles.  This is all pre-birth.  In this 
fashion, it is unnecessary to store any synaptic efficacies in the 
genes.  This approach was inspired by LinskerÕs simulations of visual 
cortex, which gave rise to on-center-off-surround cells, orientation-
selective cells, and so on, when exposed only to noise.  The crucial 
aspects of the networks in this case are their architecture Ñ layered 
receptive fields in LinskerÕs case, evolved arbitrary topology in PW Ñ 
and the learning rule Ñ Hebbian learning in both cases.

	It was debated whether to update all the organismsÕ brains 
synchronously or not.  That is, whether each organismÕs neural 
network should be allowed to make a complete neural activation and 
synaptic learning pass with each time step.  Even though it was 
desired to penalize organisms that evolved additional neurons and 
synapses, synchronous updating was ultimately selected, primarily 
because the corresponding structures in nature are executed in 
parallel, and penalties based on their serial implementation would be 
excessive.  The penalty is more properly derived from the additional 
energy use associated with these additional neural structures.

	At each time step, the input neurons are set to the appropriate 
values, corresponding to the organismÕs visual field, its current 
health-energy level, and a random number.  New neuronal 
activations are computed by the simple formulae:

xi   =   S ajt sijt

ait+1   =   1 / (1 + e-axi)

where ajt is the neuronal activation of neuron j at time t (the 
beginning of this time step), sijt is the synaptic efficacy from neuron 
j to neuron i at time t, ait+1 is the neuronal activation of neuron i at 
time t+1 (the end of this time step), and a is a specifiable logistic 
slope.

	The synaptic efficacies are then updated according to a Hebb 
rule, as:

sijt+1   =   sijt   +   hckl (ait+1 - 0.5) (ajt - 0.5)

where sijt+1 is the synaptic efficacy from neuron j to neuron i at time 
t+1, and hckl is the learning rate for connections of type c (e-e, e-i, i-
i, or i-e) from group l to group k.  An optional multiplicative decay 
term may also be applied to the synaptic efficacy.

	This simple Òsumming and squashingÓ neuron and Hebbian 
update rule are certainly coarse abstractions of the complexities 
observed in real neural systems.  Credence is lent to these particular 
abstractions by the previously quoted simulation work of Linsker, 
Pearson, and others, and by LinskerÕs and othersÕ information-
theoretic analytical work on such systems, which suggest that they 
may capture the information-processing attributes of real neural 
systems, if not their precise method of action.  These neuronal and 
learning models were selected for use in PW based on these results 
and the modelsÕ computational tractability. 

	During the course of a simulation, neural and synaptic activities 
may be monitored for a number of organisms in the world (the top 
five ÒfittestÓ, according to the ad hoc fitness function discussed 
earlier, even if it is not being used to create new organisms).  An 
example is shown in Figure 2.  Gray-scale is used to denote neural 
activation (between 0.0 and 1.0) and synaptic efficacy (between 
-maxEfficacy and +maxEfficacy).  At the very bottom of the grid is 
the color vision buffer.  The neural activations at the beginning of 
this time step are shown in a horizontal row just above the color 
vision, along with the red, green, and blue input neuron activation 
levels, and the energy and random input neuron activation levels.  
White frames are drawn around each neuronal group, except for the 
vision neurons which are framed in their corresponding color.  Black 
frames are drawn around each synapse, hence the unframed areas 
are regions of null connectivity.  Synapses that appear brighter than 
the neutral gray background are excitatory; those that appear darker 
than the background are inhibitory.  The leftmost vertical bar shows 
neuronal biases.  The non-input neural activations at the end of this 
time step are shown in the adjacent vertical bar; again, neuronal 
groups are framed in white.  Hence the diagram may be read as an 
incomplete crossbar connecting the neuronal states at the beginning 
of the time step (horizontally) to those at the end of the time step 
(vertically), through the various synaptic connections.

	Early simulations with PW had much simpler, fully recurrent 
neural architectures.  Though not particularly representative of real 
biological neural architectures, acceptable behavior strategies were 
evolved, and some of the results being presented are from organisms 
using these early networks.


7. Vision

	The color vision supplied as input to the organism is first 
rendered at the minimum window size permitted on the Iris + 1 
(because even-sized buffers can be accessed faster), or 22 x 22 
pixels.  The pixel row just above vertical center is then properly anti-
aliased into whatever number of visual neurons an organism has.  
Even though organisms and the environment of PW are three-
dimensional, the organismsÕ vision consists of just this one-
dimensional strip of pixels, rather than the complete pixel map.  
Since the organisms are confined to motion on a ground-plane, it was 
felt that the benefit derived from computational efficiency 
outweighed the small loss of information resulting from this 
restriction.

	As was discussed above in the Genetics section, the number of 
neurons devoted to each of the color components is evolved 
independently (though they are adjacent on the genome, and so may 
tend to crossover together).

	As was discussed in the Neurons and Learning section, an 
organismÕs vision is shown in the display of the brain internals that 
may be invoked interactively for some of the ÒfittestÓ individuals.  In 
addition, the full 22 x 22 pixel map for each of the organisms is 
usually displayed at the top of the screen.  This is mostly for a 
Òreality checkÓ Ñ visual reassurance that the organisms are seeing 
what they would be expected to see, and may be disabled for a slight 
speed gain.

	The vertical field of view of the organisms is fixed at 10o, since 
they only see a strip of pixels just above the center of the image.  
Their horizontal field of view, however, is under their own 
ÒvolitionalÓ, neural control.  That is, the activation of the focusing 
neuron is mapped between a minimum and maximum field of view 
(typically 20o to 120o).  In principle, this might permit some depth 
of field determinations based on cyclic focusing operations, though 
nothing so sophisticated has emerged (or is expected to emerge) in 
the limited neural systems employed by the organisms so far.

	This type of direct perception of the environment should 
answer one of cognitive psychologyÕs most frequently sounded 
complaints against traditional AI:  The organisms of PW are 
ÒgroundedÓ in their environment by their sense of vision.


8. Behavior

	A suite of primitive behaviors is made available to all 
organisms in PW, namely:

	¥ eating
	¥ mating
	¥ fighting
	¥ moving
	¥ turning
	¥ focusing
	¥ lighting

	All of these behaviors are expressed by raising the activation 
level of a prespecified neuron in the brain.  Given computational 
constraints, it was felt that a minimum number of cycles should be 
devoted to motor activity, hence this simple one-neuron-one-
behavior mapping.  The first three behaviors, eating, mating, and 
fighting, all have some associated threshold that must be exceeded 
before the activity is initiated.  Energy is expended by each of the 
behaviors, including eating.  The energy expenditure rates are 
controllable by scale factors (see Physiology and Metabolism) in the 
Òworld fileÓ (see The New Context).

	Eating is an organismÕs method for replenishing depleted 
energy stores.  In order to eat, an organismÕs position must cause it 
to overlap a piece of food.  The amount of energy consumed is 
proportional to the activation of the eating neuron, once that 
activation exceeds a specifiable threshold.

	Mating is an organismÕs method for reproducing.  In order to 
reproduce, an organismÕs position must cause it to overlap another 
organism, and both organisms must express their mating behavior in 
excess of a specifiable threshold.  The outcome of the reproductive 
attempt may be affected by the miscegenation function (see 
Genetics), or by the maximum number of organisms permitted in the 
world (see The New Context).  The organismÕs ÒdesireÓ to mate (the 
activation level of its mating neuron) is mapped onto its blue color 
component for display purposes; this coloration is visible to other 
organisms as well as to human observers.

	Fighting is an organismÕs method for attacking another 
organism.  In order to successfully attack the other organism, the 
attackerÕs position must cause it to overlap the attackee.  Only one 
organism need express its fighting behavior to successfully attack 
another.  The energy that is depleted from the prey is a function of 
the volitional degree of the attack (the activation of the predatorÕs 
fight neuron), the predatorÕs current health-energy level, the 
predatorÕs strength, and the predatorÕs size.  The product of these 
contributing factors from the predator is scaled by a global attack-to-
energy conversion factor to make the final determination of amount 
of energy depletion applied to the prey.  If both organisms are 
expressing their fight behavior, the same computation is carried out 
reversing the roles of predator and prey.  Each organismÕs desire to 
fight is mapped onto its red color component for display purposes; 
this coloration is visible to other organisms as well as to human 
observers.

	Moving refers to an organismÕs forward motion.  Unless an 
organism encounters a barrier, or the edge of the world, it will move 
forward by an amount proportional to the activation of its moving 
neuron.

	Turning refers to a change in an organismÕs orientation on the 
ground-plane (yaw).  An organism will turn about its y-axis by an 
amount proportional to the activation of its turning neuron.

	Focusing refers to an organismÕs control over its horizontal field 
of view.  As discussed in the Vision section, the activation of an 
organismÕs focusing neuron will be linearly mapped onto a range of 
possible angles to provide its horizontal field of view.  This makes it 
possible for an organism to use its vision to survey most of the world 
in front of it or to focus closely on smaller regions of the world.

	Lighting refers to an organismÕs control over the brightness of a 
cap of several polygons on the front face of its ÒbodyÓ.  The activation 
of an organismÕs lighting neuron is  linearly mapped onto the full 0 to 
255 brightness range in all color components of these front polygons.  
Accordingly, a simple form of visual communication is possible, in 
principle, for the organisms inhabiting PW.  (No evidence of their use 
of this form of communication has yet been found nor sought to date, 
though evidence of the organismsÕ use of vision for controlling 
locomotion has been observed.)


9. The New Context

	The ÒworldÓ of PolyWorld is a flat ground-plane, possibly 
divided up by a few impassable barriers, filled with randomly grown 
pieces of food, and inhabited by the organisms previously described.

	The number of organisms in the world is controllable by 
several means.  First, a maximum number of organisms is specifiable, 
in order to keep the problem computationally tractable.  Second, a 
minimum number of organisms is specifiable to keep the world 
populated during the early on-line GA stage (see Genetics).  Finally, 
an initial number of organisms is specifiable to determine how many 
individuals to seed the world with at the start of the simulation.

	Food is grown at a specifiable rate up to a specifiable maximum 
number of grown food items.  The number of food items may be 
guaranteed to be kept between a specifiable minimum and maximum 
food count.  Subject to this maximum, food is also generated as the 
result of an organismÕs death.  The amount of energy in a piece of 
food that is grown is randomly determined between a specifiable 
minimum and maximum food energy.  The amount of energy in a 
piece of food resulting from the death of an organism is that 
organismÕs food-value-energy (see Physiology and Metabolism) at 
death, or a specifiable minimum-food-energy-at-death.

	An arbitrary number of barriers may be placed in the world, 
which inhibit movement of the organisms.  These can serve to 
partially or completely isolate populations of organisms, and as such 
can contribute significantly to speciation (genetic diversity).  For 
reasons of computational efficiency, they are typically placed parallel 
to the z (depth) axis, though this is not strictly necessary.

	It is possible to manage the minimum, maximum, and initial 
numbers of organisms and food items, along with the ad hoc fitness 
statistics, simultaneously for a number of different independent 
ÒdomainsÓ.  These domains must be aligned parallel to the z (depth) 
axis, and typically, though not necessarily, coincide with the divisions 
imposed on the world by the barriers.  This permits the simultaneous 
ÒculturingÓ of completely independent populations when barriers 
extend the full length of the world, or limits the spread of genes 
between domains to those resulting from actual movement of 
organisms when the barriers are arranged so as to leave gaps for 
organisms to travel through.  If the domain fitness statistics were not 
kept separately, then genes from one domain could migrate to 
another domain by virtue of their global fitness during the start-up 
on-line GA phase.

	It is possible to set a flag such that the edges of the world act 
as barriers (the usual), wrap around, or arenÕt there at all.  In this 
last case, PWÕs ground-plane acts much like BraitenbergÕs table top, 
with organisms that move past the edge of the world dying instantly.

	Various monitoring and graphing tools exist to assist in 
following the progress of a simulation and in developing an 
understanding of the evolutionary and neural dynamics at work.  As 
was mentioned earlier (in the section on Neural Systems and 
Learning), a display of the internal workings of any of the five 
ÒfittestÓ organisms may be called up at any time.  In addition, a small 
window that maintains an overhead view of the world will 
automatically track that same organism upon request.  This overhead 
window may also be zoomed in and out to follow the organism more 
closely.  

	Also available are graphic displays of the time histories of 
certain quantities of interest, including:  (1) population sizes (overall 
and per domain), (2) the past maximum, current maximum, and 
current average values of the ad hoc fitness function, (3) the ratio of 
the number of organisms ÒbornÓ (by mating) to the sum of the 
number of organisms born and created, and (4) the ratio of the 
difference of food-energy in and food-energy out to the sum of these 
two values.  These last two items in particular are important gauges 
of the course of the simulation.  Item (3) will start at 0.0 and 
asymptote to 1.0 for successful simulations, in which at least one 
species has emerged with an ESS; it will peak well below 1.0 for 
unsuccessful simulations.  Item (4) ranges from -1.0 to 1.0, and 
should asymptote to 0.0, for a world where energy is conserved.  
Three values are actually plotted for item (4):  (a) the total food-
energy, including the initial seeding of the world, which starts at 1.0 
and should asymptote to 0.0, (b) the average food-energy, excluding 
the initial seeding of the world, which starts at 0.0, and rapidly 
becomes negative, but should also asymptote to 0.0, and (c) the 
current food-energy on a time-step by time-step basis, which 
fluctuates rapidly, but should cluster around the average food-
energy.

	One additional display can graphically present the results of an 
analysis of the genetic variability in the population.  All pairs of 
organisms are examined to determine the magnitude of the Hamming 
distance between them in gene space, and a gray-scale  plot is used 
to display normalized genetic distances for the entire population at 
each time step.

	All of the simulation control parameters and display options 
are defined in a single Òworld fileÓ that is read at the start of the 
simulation.  In addition, some of the display options can be invoked 
interactively at runtime.

	There isnÕt space to go into many details of the code itself.  
However, it may be worth noting that it consists of about 15,000 
lines of C++, and is entirely object oriented, except  for a single 
routine devoted to handling the organism-organism and organism-
food interactions (for reasons of computational efficiency).  The 
organisms, food, and barriers are maintained in doubly-linked lists 
sorted on a single dimension (x).  This simple data structure has 
minimal maintenance overhead, yet rules out most non-intersections 
very well, and permits a sorting algorithm to be used that capitalizes 
on the expected frame-to-frame coherency of organism positions.  It 
runs on a Silicon Graphics Iris (to take advantage of its hardware 
renderer for all the vision processing), and uses a set of object 
oriented C++ graphics routines (included in the line count above) that 
wrap around the standard Iris graphics library.


10. Results:  Speciation and Complex Emergent Behaviors

	Despite the variability inherent in different worlds, certain 
recurring ÒspeciesÓ have occurred in a number of the simulations run 
to date.  By ÒspeciesÓ, I mean groups of organisms carrying out a 
common individual behavior that results in distinctive group 
behaviors.  Since the selection of these behaviors are derived from 
the activity of their neural network brains, and the success of these 
behaviors is partially a function of their physiologies, both of which 
are in turn based on the genome of the organism, the behavioral 
differences may generally be traced to the organismÕs genetic code.  
Hence these behavioral differences are representative of different 
genetic species.

	A simulation is considered ÒsuccessfulÓ if and only if some 
number of species emerge which are capable of sustaining their 
numbers through their mating behaviors, and thus organism 
creations cease.  These species can be said to have developed an ESS 
within the ecological environment of PW.  The observational reports 
below only refer to ÒsuccessfulÓ simulations.

	The first of these species has been referred to as the "frenetic 
joggers".  In an early simulation without barriers, without a 
miscegenation function, and with borders that wrap around 
(essentially forming a torus), a population emerged that basically just 
ran straight ahead at full speed, always wanting to mate and always 
wanting to eat.  That particular world happened to be benign enough, 
that it turned out they would run into pieces of food or each other 
often enough to sustain themselves and to reproduce.  It was an 
adequate, if not particularly interesting solution for that world.  And 
without the miscegenation function or any physical isolation due to 
barriers, whatever diversity was present in the early world 
population was quickly redistributed and blended into a single 
species that completely dominated the world for as long as the 
simulation was run.

	The second recurring species has been referred to as the 
Òindolent cannibalsÓ.  These organisms "solve" the world energy and 
reproduction problem by turning the world into an almost zero-
dimensional point.  That is, they never travel very far from either 
their parents or their offspring.  These organisms mate with each 
other, fight with each other, kill each other, and eat each other when 
they die.  They were most prevalent in simulations run before the 
parents were required to transfer their own energies to the 
offspring; the organisms of these worlds were exploiting an 
essentially free energy source.  With proper energy balancing, this 
behavior was reduced to only an occasional flare-up near corners of 
the world, where some organisms with limited motor skills naturally 
end up congregating, sometimes for quite extended periods of time.  
It turns out that the primary evolutionary benefit associated with 
this behavior was the ready availability of mates, rather than the 
ÒcannibalisticÓ food supply.  This was determined by completely 
eliminating the food normally left behind by an organismÕs death, yet 
still observing the emergence of such species.  Large colonies of these 
indolent cannibals look from above like a continuous (non-gridded) 
version of ConwayÕs game of LIFE.

	The third recurring species has been referred to as the Òedge 
runnersÓ.  These organisms take the next step up from the cannibals, 
and essentially reduce their world to an approximately one-
dimensional curve.  They mostly just run around and around the 
edge of the world (which they are forcibly prevented from running 
off of in most of the simulations).  This turns out to be a fairly good 
strategy, since, if enough other organisms are doing it, then some will 
have died along the path, ensuring adequate supplies of food.  And 
mates are easily found by simply running a little faster or a little 
slower, running in the opposite direction, or simply stopping at some 
point and waiting for other runners to arrive (all of which behaviors 
have been observed).  A form of this behavior persists even when 
barriers block access to the rest of the world; organisms still 
sometimes congregate along any edges, including the barriers.  It has 
been suggested [22] that this may be a form of behavioral isolation, 
permitting this species to retain its genetic identity to the exclusion 
of other species.

	Another species recently emerged as the first evolutionarily 
stable solution to a Òtable topÓ world Ñ one with no edges.  These 
ÒdervishesÓ evolved a simple rapid-turning strategy that kept them 
away from the dangerous edges of the world, and yet explored 
enough of the world to bring them into contact with food and each 
other.  While this basic behavioral strategy persisted for many 
hundreds of generations, evolution continued to explore optimum 
degrees of predation, in a sort of continuous prisonerÕs dilemma over 
optimum degrees of cooperation.  Waves of varying levels of 
expression of the fighting behavior could be observed sweeping 
through several 

distinct populations over evolutionary time-scales, with the greatest 
variation in behaviors clearly seen at the boundaries between these 
populations.

	The most interesting species and individuals are not so easily 
classified.  In some worlds where a single species has become 
dominant, the individualsÕ behaviors have still been quite varied.  
And in many worlds, no single species becomes obviously dominant.  
It is especially in these simulations that a number of complex, 
emergent behaviors have been observed, including:
	1) responding to visual stimuli by speeding up,
	2) responding to an attack by running away (speeding up),
	3) responding to an attack by fighting back,
	4) grazing (slowing upon encountering each food patch),
	5) expressing an attraction to food (seeking out and circling 
food), and
	6) following other organisms.

	The first item is important in that it implies that conditions 
have been found that will cause evolution to select for the use of the 
organismsÕ vision systems.  All four of the earlier, simpler speciesÕ 
behaviors would be appropriate even if these vision systems did not 
exist.  Yet PW was built on the assumption that vision would be a 
powerful, useful sense mechanism that evolution could not fail to 
employ.  Even a simple speeding up in response to visual stimulation 
could result in reaching food or a potential mate more effectively, 
and this was the first observed visual response to emerge.

	The second and third items both represent reasonable 
responses to attack by a predator.  Fleeing may reduce the effect of 
the attack, and fighting back is an energy-efficient use of the 
organismÕs own ability to fight (as opposed to expressing the fight 
behavior continuously which would expend unnecessary energy).

	Strategies four and five represent efficient feeding strategies.  
As simple a survival skill as grazing might seem - to simply notice 
when oneÕs internal energy is going up, and cease moving until it 
stops going up - it was not observed until a fairly recent simulation.  
It is still not a wide-spread phenomenon, though a few instances 
have now been observed.  Only the most recent simulation, as of this 
writing, has given rise to a population of organisms that seem to be 
able to actively seek out food and ÒorbitÓ it while eating; such 
ÒforagingÓ is clearly a valuable survival trait.  These organisms 
appear to be drawn to the food as if there were a magnet or some 
point attractor located in the food and controlling the organismsÕ 
behavior, though no such mechanism exists in PW.  Their attraction 
to the food is purely a result of selection forces acting on the the 
neural architecture connecting their vision systems to their motor 
systems.

	The final, ÒfollowingÓ strategy has also emerged only in this 
most recent simulation.  Clearly of value, whether for seeking a prey 
or a mate, this represents the most complex coupling of the vision 
sense mechanism to the organismsÕ motor controls yet observed.  
Small ÒswarmsÓ of organisms, and one example of a few organisms 
ÒchasingÓ each other were even suggestive of simple ÒflockingÓ 
behaviors.

	All of these behaviors, being inherently temporal phenomena, 
require some sort of temporal medium for display.  Short video clips 
of most of the above species and behaviors should be available in a 
companion videotape released by the publisher of this book.



11. Concluding Remarks

	Real benefits have already begun to accrue from the studies of 
artificial neural systems.  Meanwhile, the study of artificial evolution 
Ñ genetic algorithms Ñ is yielding insights into problems of 
optimization, and into the dynamics of natural selection.  One form of 
the study of Artificial Life is the perhaps obvious combination of 
these two fields of research.  Adding computer graphics visualization 
techniques yields the basic substrate of PolyWorld.

	One of the primary goals set out for PW has already been met:  
the evolution of complex emergent behaviors from only the simple 
suite of primitive behaviors built into the organisms of PW, their 
sense mechanisms, and the action of natural selection on their neural 
systems.  These recognizable behavioral strategies from real living 
organisms, such as ÒfleeingÓ, Òfighting backÓ, ÒgrazingÓ, ÒforagingÓ, 
ÒfollowingÓ, and ÒflockingÓ, are purely emergent in the PW 
environment.  And built as they are from simple, known primitive 
behaviors, in response to simple, understandable ecological 
pressures, they may be able to remove a little bit of the mystery, if 
not the wonder, at the evolution of such behaviors in natural 
organisms.  Indeed, the organisms of PW have evolved these higher-
order behaviors by reproducing the same bottom-up complexity 
ordering of behavioral dynamics as are postulated to drive such 
phenomena in natural organisms:  core motor controls, followed by 
approach and avoidance behaviors in individuals, followed by group 
flocking behaviors that emerge naturally from the individual 
approach/avoidance behaviors.

	The simple but effective strategies evolved by organisms in the 
earlier, simpler simulations may be valuable as sort of Ònull 
hypothesesÓ about certain forms of animal behavior.  In particular, 
aggregation and wall-following amongst these simple organisms 
occurs without need for elaborate behavioral strategies.  It is 
sufficient that corners and walls obstruct simpler trajectories.  Yet if 
enough organisms occupy these locational niches, it becomes a 
behavioral niche as well, by providing readily available mates, and 
an easily achieved form of behavioral isolation.

	It is, perhaps, easier to contemplate and understand these 
behaviors in the simulated organisms of PW than it is in natural 
organisms, precisely because they are simulated.  The blessing and 
the curse of Artificial Life is that it is much more difficult for humans 
to anthropomorphize (zoomorphize?  biomorphize?) these organisms 
in a machine than it is natural organisms.  This frees us from 
prejudices and preconceptions when observing and analyzing the 
behaviors of artificial organisms, yet the most highly motivated of 
ALife researchers is going to find it difficult to look at an artificial 
organism and declare it unequivocally alive.  

	As more and more sophisticated computational models of living 
systems are developed, it will be only natural to ask whether they 
are in fact really alive.  To answer this, however, requires a 
resolution to probably the greatest unanswered question posed and 
addressed by the study of Artificial Life:  ÒWhat is life?Ó  Farmer & 
Belin offer an analogous question for consideration:  ÒIf we voyage to 
another planet, how will we know whether or not life is present?Ó  
One might also ask:  If we ÒvoyageÓ to an artificial world, how will we 
know whether or not life is present?  Rather than just ignore this 
question, letÕs look briefly at the how the organisms in PolyWorld 
stack up against Farmer & BelinÕs list of Òproperties that we associate 
with lifeÓ (slightly abridged here for brevity):


	¥ ÒLife is a pattern in spacetime, rather than a specific material 
object.Ó
		PW organisms are indeed patterns in a computer, rather 
than any specific
		material; they neither extend nor violate this first 
condition.
	¥ ÒSelf-reproduction.Ó
		PW organisms certainly reproduce within the context of 
their world.
	¥ ÒInformation storage of a self-representation.Ó
		PW organisms use an analog of the same storage 
mechanism Farmer & Belin
		mention for natural organisms:  their genetic 
representation.
	¥ ÒA metabolism.Ó
		A PW organismÕs metabolism effectively converts food 
found in the
		environment into the energy it needs to carry out its 
internal processes
		and behavioral activities, just as is the case in natural 
organisms.  The
		metabolism in PW organisms is much simpler, but if the 
function is the
		same, does the complexity of the underlying process 
matter?
	¥ ÒFunctional interactions with the environment.Ó
		PW organisms interact with their environment, including 
other organisms;
		the more sophisticated ones respond behaviorally to 
changes in the
		environment; such responses are purely under the 
control of the
		organism.
	¥ ÒInterdependence of parts.Ó
		Following Farmer & BerlinÕs reasoning, PW organisms can 
and would die
		were they somehow separated from their internal energy 
store.  And
		severing an organismÕs brain in two would not produce 
two organisms
		with behavior anything like the original.  Unnecessarily 
(and perhaps
		inappropriately) stepping outside the bounds of the 
simulation, they would
		also die if their various procedures and data were 
destroyed or isolated.  In
		either case, half an organism is no longer that organism, if 
it is any
		organism at all.
	¥ ÒStability under perturbations.Ó
		PW organisms can survive small changes to their 
environment.  Indeed,
		whole species have reemerged in entirely different 
simulations.  Again
		stepping outside the simulation, whole species have 
emerged with and
		without any of a variety of errors in the code.
	¥ ÒThe ability to evolve.Ó
		PW organisms clearly can and do evolve.  There are 
undoubtedly limits to
		their evolution; e.g., they could not possibly evolve a 
sense of smell without
		programmer intervention.  However, all natural 
organisms we know of
		have limits to their evolutionary capabilities:  It is highly 
unlikely that
		humans could evolve a steel appendage; if Einstein is 
correct, it is absolutely
		impossible for them to evolve a method of personal 
locomotion that would
		exceed the speed of light.  All organisms, natural or 
artificial, are bound
		by the physics of their universe.  Similar to the question 
about metabolism,
		does the complexity of the underlying physics matter?

Somewhat surprisingly, then, it would seem that we either need to 
further refine our constraints on the definition of life, or welcome a 
new genus to the world.

	Ultimately, the resolution to this question of life in artificial 
organisms is probably going to have to be based on a consensus, as 
with TuringÕs famous test for artificial intelligence.  Perhaps in this 
case, however, the consensus of a knowledgeable and informed jury 
is needed, rather than that of TuringÕs unspecified, presumably 
average group of individuals.  As with the debate about the 
ÒalivenessÓ of natural viruses being properly resident with biologists, 
the question of ÒalivenessÓ in artificial organisms is probably best 
argued by a combination of computer-aware biologists and biology-
aware computer scientists.


12. Future Directions

	The various species and behaviors that have emerged in the 
different simulations suggest that PW may be a rich enough 
simulation environment to pursue further evolutionary studies.  In 
particular, a way of sort of ÒbenchmarkingÓ PW Ñ the way one 
compares the results of a computational fluid dynamics code to flow 
over a flat plate or a cylinder, or over an airfoil measured in a 
windtunnel Ñ may have presented itself in the form of optimal 
foraging strategies as studied in the field of behavioral ecology.  A 
simple, canonical foraging experiment has been defined and 
analyzed, and is now being simulated with PW.  Agreement or 
disagreement with the analytical model should be examined and 
understood.

	The neural architectures that provide the most useful survival 
strategies should be analyzed and understood.  It would also be 
fairly straightforward to encode an entire range of learning 
algorithms in the genes of the organisms in PW, and attempt to 
evolve the most effective learning algorithm, rather than assuming it 
to be Hebbian.  (Some consideration has even been given the 
possibility of having the fundamental genetic representation of 
information Ñ the genetic code Ñ evolve.)  At least it might 
worthwhile implementing cluster-to-cluster initial connection 
strengths, initial connection strength variances, and maximum 
connection strengths, to begin to hint at distinct cell types.  Or it may 
be more worthwhile to jump directly to a more sophisticated cell 
model, capable of capturing the actual temporal dynamics of spike 
trains rather than average firing rates.

	More environmental interactions should be supported, 
including the ability for the organisms to pick up, carry, and drop 
pieces of food, and perhaps even pieces of barrier material.  This 
should yield useful reasons for organisms to cooperate, other than 
simply to reproduce.

	Though not discussed in the earlier parts of the paper, 
energetics of the system have been observed to be crucial to the 
evolution of successful survival strategies.  Mimicing the differences 
between energy-rich tropical zones and energy-starved polar zones 
in our one known, natural ecosystem, artificial life flourishes in 
energy-rich simulations, and languishes in energy-starved 
simulations.  Perhaps someday it may be possible to make useful 
predictions about viable ranges of energy flux for natural systems 
from artificial ecologies like PW.

	A quantitative assessment of the degree to which the isolation 
of populations affects speciation may be possible with PW.  Some 
tentative first steps have already been taken in this direction, though 
questions remain about the most appropriate comparisons to make 
and the appropriate times at which to make these comparisons. This 
coupled with the problems associated with assuring the emergence of 
an ESS in every population, and the simple magnitude of processing 
time required to perform the simulations has delayed a complete 
series of experiments of this nature.

	There are thousands of other interesting experiments that one 
might perform with this system, including:  Monitoring brain size in 
otherwise stable populations, such as the "dervishes"...  are smaller 
and smaller nervous systems actually being selected for?  Monitoring 
the frequency and magnitude of attacks on other organisms as a 
function of their genetic (dis)similarity.  Monitoring the amount of 
energy given to offspring in a single species... is there any indication 
of an asymmetric split into different relative contributions?  Hand-
tailoring a good neural architecture or two and seeding the world 
with these engineered organisms.  Providing multiple internal, neural 
time-cycles per external, action time-cycle.  Evolving three 
completely independent domains of organisms, with barriers in 
place, and then removing the barriers to observer the interspecies 
dynamics.  It may even be possible to model the entire population of 
Orca whales that frequent the waters around Vancouver, and look for 
an evolutionary split into pods that travel little and eat essentially 
stationary food sources versus pods that travel widely and feed on 
fish, a very mobile food source.  And on and on.  In hopes that others 
may find PolyWorld to be a useful tool for exploring these kinds of 
questions, it has been made available via ftp from ftp.apple.com 
(130.43.2.3) in /pub/polyworld.  Complete source code and some 
sample "world files" are provided.

	In a more fanciful, and perhaps more visionary vein, it is 
hoped that, someday, one of the organisms in PolyWorld that 
demonstrates all the survival behaviors observed to date, plus a few 
others, could be transferred from its original environment to, say, a 
maze world, and become the subject of some classical conditioning 
experiments.  KlopfÕs [24,25] success at demonstrating over 15 
classical conditioning phenomena in a single neuron using differential 
Hebbian learning (he called it Òdrive-reinforcementÓ learning), 
strongly suggests that such phenomena should be demonstrable in 
PolyWorldÕs organisms.

	And then, of course, there is simply Òmore, bigger, and longerÓ:  
More organisms, with bigger neural systems, evolving longer.  As a 
gedanken experiment, consider just how much Òmore, bigger, and 
longerÓ might be useful:  The current 102 organisms, 102 neurons, 
and 102 generations (approximately), could be expanded to 106 
organisms, neurons, and generations, through an increase in compute 
power of about 1012.  (Though this sounds like a tremendous 
increase to ask for, consider that the current simulation is running on 
a single, scalar workstation processor, not a vectorized, massively 
parallel processor, then extend todayÕs trends in compute power, and 
this ceases to be such a daunting request; in fact, the compute power 
may be significantly less than this due to the greatly reduced motor 
and autonomic nervous systems that would be required by artificial 
organisms.)  It turns out that this is a fairly reasonable amount of 
compute power with which to consider modeling a complete human 
brain Ñ basically devoting one of todayÕs fast computers to every 
neuron Ñ but no one understands how to actually construct such an 
artificial brain.  However, this same amount of compute power might 
be used to evolve the equivalent of a new species of computational 
lab rat every week... and this is how:  by combining evolution, neural 
systems, and ecological dynamics.  The leap of (informed) faith is 
this:  If it is actually possible to evolve a computational lab rat Ñ and 
the experiments so far in PW suggest that this may indeed be the 
case Ñ then  it will be possible to evolve human or higher levels of 
intelligence by the very same methods.  At least with this approach 
there can be milestones and benchmarks along the path to human 
level intelligence in a machine.

	If there is any question about why one would wish to pursue 
these research directions, it is always possible to point to the benefits 
to be derived in the evolutionary, ecological, biological, ethological, 
and even computer science fields.  But it may also turn out to be the 
only ÒrightÓ way to approach machine intelligence.  One view of 
intelligence is as an evolved, adaptive response to a variable 
environment, that due to historical constraints and opportunism on 
the part of nature happens to be based upon neuronal cells.  One 
might further recognize that intelligence is really more a near-
continuum Ñ a spectrum from the simplest organism to the most 
complex Ñ rather than some singular event unique to humans.  Then, 
by utilizing both the method (Natural Selection) and the tools 
(assemblies of neuronal cells) used in the creation of natural 
intelligence, PolyWorld is an attempt to take the appropriate first 
steps towards modeling, understanding, and reproducing the 
phenomenon of intelligence.  For while one of the grand goals is 
certainly the development of a functioning human level (or greater) 
intelligence in the computer, it would be an only slightly less grand 
achievement to evolve a computational Aplysia that was fully 
knowable Ñ fully instrumentable, and, ultimately, fully 
understandable Ñ to let us know that we are on the right scientific 
path.


Acknowledgements

	The author would like to thank Alan Kay and Ann Marion of 
AppleÕs Vivarium Program for their support and encouragement of 
this admittedly exotic research.  He would also like to thank his wife, 
Levi Thomas, for her patience, understanding, and support 
throughout the project.


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