weld@CS.WASHINGTON.EDU (Dan Weld) (11/02/90)
Date: Wed, 31 Oct 90 18:32:23 GMT
From: Phil Husbands <philh@cogs.susx.ac.uk>
To: announce@cogs.susx.ac.uk
Subject: Alife seminar, Fri. 9th Nov.
Tom Ray, an associate professor of biology at the University of Delaware, will
be visiting Sussex and has agreed to give a talk on his work in Artificial
Life. The talk will be at 2.30pm in PB1A7 on Fri. 9th Nov. Abstract follows.
An Approach to the Synthesis of Life:
evolution and ecology of digital organisms
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Digital organisms have been synthesized based on a computer metaphor of
organic life in which CPU time is the ``energy'' resource and memory is the
``material'' resource. Memory is organized into informational ``genetic''
patterns that exploit CPU time for self-replication. Mutation generates new
forms, and evolution proceeds by natural selection as different ``genotypes''
compete for CPU time and memory space, exploiting one another for
informational and energetic resources.
The digital organisms are self-replicating computer programs, however,
they can not escape because they run exclusively on a virtual computer in
its unique assembler language. From a single ancestral ``creature'' there
have evolved tens of thousands of self-replicating genotypes of hundreds
of genome size classes. Parasites evolved, then creatures that were
immune to parasites, and then parasites that could circumvent the immunity.
Hyper-parasites evolved which subvert parasites to their own reproduction
and drive them to extinction. The resulting genetically uniform communities
evolve sociality in the sense of creatures that can only reproduce in
cooperative aggregations, and these aggregations are then invaded by cheating
hyper-hyper-parasites.
Diverse ecological communities have emerged. These digital communities have
been used to experimentally study ecological and evolutionary processes:
e.g., competitive exclusion and coexistence, symbiosis, host/parasite
density dependent population regulation, the effect of parasites in enhancing
community diversity, evolutionary arms races, punctuated equilibrium, adaptive
landscapes, and the role of chance and historical factors in evolution.
It is possible to extract information on any aspect of the system without
disturbing it, from phylogeny or community structure through time to the
``genetic makeup'' and ``metabolic processes'' of individuals. Digital
life demonstrates the power of the computational approach to science as a
complement to the traditional approaches of experiment, and theory based on
analysis through calculus and differential equations.