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Artificial Life (ALife), as an area of investigations, took
its form in the late 1980s [1,2]. The main motivation of ALife is
to model and understand the formal rules of life. As C.G. Langton
said, ...the principle assumption made in Artificial Life
is that the logical form of an organism can be
separated from its material basis of construction .
ALife organisms are man-made, imaginary entities,
living in computer-program worlds. Evolution, ecology, and the
emergence of new features of life-like creatures are under
special attention of the ALife researches.
The ALife evolutionary models include:
by L. Yaeger: a computer model of artificial
organisms, which have structured neural
networks, possess a color vision, can move and increase
their energy resources by eating foods, can mate and
fight with each others. Some kinds of non-trivial
ecological strategies were evolutionary emerging during PolyWorld simulations.
- Tierraby T. Ray: a model of the evolution of self-reproductive programs. The Tierras organisms include genome strings, which determine the executive program instructions. The organisms are able to exchange the program code segments. The interactions between the organisms result in an evolutionary emergence of complex biodiversity of the self-reproductive programs.
- Avida by C. Adami et al. is an Tierra-inspired model. Basing on Tierra and Avida, C. Adami et al. constructed a mathematical model describing the distribution of species in evolving populations. This model quantitatively supports the point of view that the evolution has a punctuated character rather than gradual one.
- The analysis of interactions between learning and evolution by D. Ackley and M. Littman. This work expressively demonstrated that learning and evolution together were more successful than either alone in producing adaptive populations [2, pp.487-509].
- ECHO by J.H. Holland. This model describes the evolution of simple agents, which can interact by mating, fighting, and trading. The interaction between agents results in complex ecologies: arms race, symbiotic relations, etc.
- The model of co-evolution of host and parasite populations by D. Hillis [2, pp.313-324]. The individuals of the host population in this model are algorithms, which are intended to solve a certain practical problem (e.g. the sorting problem), whereas the parasite population is a set of tasks to be solved. The host population evolves to find a good solution of the problem, while the parasite population evolves to make the problem more difficult. The competitive host-parasite co-evolution ensures finding the significantly better solution, than the host population could find alone.
- Models of evolving cellular automata, e.g. the models by M. Mitchell et al., describing the evolutionary search of cellular automata, which can perform simple computations .
- AntFarm by R.J. Collins and D.R. Jefferson is a Connection-Machine-based model for simulation of foraging behavior in huge evolving populations of artificial ants [2, pp. 579-601].
- Classifier system by J.H. Holland et al is a model of evolving cognitive process . The classifier system models the inductive inference scheme, which is based on a set of logic rules. Each rule has the following form: if <condition> then <action>. The rule system is optimized by both learning and evolutionary search. Learning implies that the priorities of the use of the particular rules (the rule strengths) are modified by means of so-called backed-brigade algorithm. The evolutionary search is the discovery of new rules by means of the genetic algorithm.
ALife evolutionary modeling is currently developing field of
evolutionary investigations. Mainly, the models are clever
computer experiments. The serious mathematical description of
ALife evolution features is still at the very beginning. A good
example of significant mathematical investigations is
Adamis model of species-size distribution in evolving
populations . This model is based on the theory of
Self-Organizing-Criticality  and provides a
reasonable interpretation of both ALife computer experiments (on
Tierra and Avida) and real biological data.
ALife evolutionary modeling is developing in close relations
with life origin models, Kauffmans NK-automata investigations,
and researches of evolutionary algorithms. Adaptive behavior of ALife organisms is often based
on operation of artificial neural networks; the evolution
is mainly modeled by means of the genetic
algorithms. It should be noted that in contrast to the
original version of the applied genetic algorithm the
majority of ALife evolutionary models dont include explicit
fitness function. The fitness is usually endogenous: the
organisms are naturally born (when their parents are ready to
give birth to the children) and die (e.g. through starving or
being killed by predators).
ALife modeling throws a new light upon evolutionary phenomena.
An excellent example is investigations of the Baldwin effect
. According to the Baldwin effect, the learned features of
organisms could be indirectly inhered in subsequent generations.
The Baldwin effect works in two steps. At the first step evolving
organisms obtain (through appropriate mutations) an ability to
learn a certain advantageous trait. The fitness of such organisms
is increased; hence they are spread throughout the population.
But learning is typically costly for an individual, because it
requires energy and time. Therefore the second step (which is
called the genetic assimilation) is possible: the
advantageous trait can be reinvented by the genetic
evolution and become directly genetically encoded. The second
step takes a number of generations; a stable environment and a
high correlation between genotype and phenotype facilitate this
step. Thus, the advantageous trait that has been originally
acquired can become inherited, though the evolution is of
A number of researchers (G.E. Hinton & S.J. Nowlan, 1987,
D. Ackley & M. Littman, 1992, G. Maylay, 1996) modeled the
Baldwin effect. They demonstrated that the Baldwin effect can
play the important role during the evolution of ALife organisms.
See collections of papers [8,9] for details.
There is an obvious tendency towards modeling of evolution of
cognition abilities. Whereas the evolutionary theories developed
in the first part of 20-th century described evolution processes
in terms of distributions of gene frequencies (see the node General Models of Evolution for
details), the current ALife evolutionary models are actively
incorporating such notions as learning, neural networks, adaptive
behavior. This tendency is certainly supported by investigations
in computer sciences, cognitive sciences, artificial
intelligence; so we can conclude that the mention tendency
isnt accidental and the evolution of cognition features
will be intensively investigated in the future.
The Internet resources of ALife evolutionary models can be
found at Santa Fe Institute
Artificial Life Site.
Conclusion. ALife evolutionary modeling is a rather
new, very interesting area of evolutionary investigations. It is
trying to find the formal rules, the formal laws of life and
evolution by means of computer modeling of life-like entities.
The ALife models are able to throw a new light upon old
evolutionary problems (the Baldwin effect, the punctuated
character of the evolution process). ALife evolutionary
investigations have good perspectives, especially in the field of
analysis of the evolution of cognition abilities in natural and
1. Langton, C. G. (Ed.) (1989). Artificial
Life. Reading, MA: Addison-Wesley.
2. Langton, C. G., Taylor, C., Farmer, J. D., and Rasmussen,
S. (Eds.) (1992). Artificial
Life II. Reading, MA: Addison-Wesley.
3. Mitchell, M., Crutchfield, J.P., Das, R. Evolving
Cellular Automata with Genetic Algorithms: A Review of Recent
Work // In Proc. of the First International Conference on
Evolutionary Computation and Its Applications (EvCA'96). Moscow,
Russia: Russian Academy of Sciences, 1996.
4. Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.
Processes of Inference, Learning, and Discovery. Cambridge,
MA: MIT Press.
5. Adami, C., Seki, R., Yirdaw, R. Critical Exponent
of Species-Size Distribution in Evolution // In Adami, C.,
Belew, R., Kitano, H., Taylor, C., (Eds.) (1998). Artificial Life
VI. MIT Press, pp. 221-227.
6. Bak, P. (1996). How
Nature Works: The Science of Self-Organized Criticality,
7. Baldwin, J.M. A
new factor in evolution // American Naturalist, 1896. V.30,
8. Belew, R.K. and Mitchell, M. (Eds.) (1996). Adaptive
Individuals in Evolving Populations: Models and Algorithms,
9. Turney, P., Whitley, D., Anderson, R. (Eds.). Evolution, Learning,
and Instinct: 100 Years of the Baldwin Effect // Special
Issue of Evolutionary Computation on the Baldwin Effect, V.4,
See also: Links on Complexity, Self-Organization and Artifical Life