284 lines
15 KiB
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284 lines
15 KiB
Plaintext
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91-03/ALIFE.inf
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From: ray@chopin.udel.edu (Thomas Ray)
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Subject: Synthetic Life (evolving computer worms) (LONG)
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Date: 17 Mar 91 20:46:33 GMT
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Organization: University of Delaware
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[MODERATOR'S NOTE: The following is posted as a test of this group's
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interest in the topic at hand, "artificial life" (i.e., "organisms"
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that dwell in programs running on computers). I would appreciate your
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response, via private email to me, regarding how useful or not you find
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this information.
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[Thomas originally posted this information for a group studying artificial
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life at the University of Delaware, so there is some site-specific reportage
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at the end which you may or may not find useful. -- Bob Jacobson]
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Peter Arensburger writes:
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> A couple of days ago I was listening to a talk by Richard Dawkins about
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> modeling evolutionary processes on a computer. He mentioned an experiment
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> by Thomas Ray in which small (40 instructions long) autoreproducing programs
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> where allowed to spread freely in a certain amount of memory. Then, by
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> randomly mutating some of the programs you could see mutant programs
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> become better adapted for reproduction.
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...
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> Has anyone heard about this experiment? If so please answer by e-mail.
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The work Peter describes is in press, and should be available in May:
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Ray, T. S. In Press. An approach to the synthesis of life.
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In: Artificial Life II, Santa Fe Institute Studies in the Sciences of
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Complexity, vol. XI, (Farmer, J. D., C. Langton, S. Rasmussen, & C. Taylor,
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eds). Redwood City, CA: Addison-Wesley, 1991.
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Although I don't have a paper in it, you might be interested in the
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following book which is available now:
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Langton, Christopher G. [ed.]. 1989. Artificial life: proceedings of an
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interdisciplinary workshop on the synthesis and simulation of living systems.
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Vol. VI in the series: Santa Fe Institute studies in the sciences of
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complexity. Addison-Wesley.
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Also, I will present the work in a seminar at Princeton (biology) on
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April 5, 1991.
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If anyone can't wait till May, I could email them a LaTeX version
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of the manuscript. Below I attach an abstract, and then a summary of
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the current activities of my research group.
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This is a slightly expanded version of an abstract describing this work, which
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was submitted to:
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European Society for Evolutionary Biology, Third Congress. Debrecen, Hungary
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September 2 - 6. Contact: Dr. Liz Pasztor - Department of Genetics -
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Eotvos University 1088 Budapest - Muzeum krt. 4/a. - Hungary.
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-------------------------begin abstract-----------------------------------
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Synthetic Life: co-evolution in digital organisms.
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THOMAS S. RAY. University of Delaware, Newark, DE, 19716, USA,
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ray@brahms.udel.edu. 302-451-2753
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Ideally, the science of biology should embrace all forms of life. However
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in practice, it has been restricted to the study of a single instance of
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life, life on earth. Because our science of biology is based on a sample
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size of one, we can not know what features of life are peculiar to earth,
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and what features are general, characteristic of all life. A practical
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alternative to a truly comparative inter-planetary biology, is to create
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synthetic life. Evolution in a bottle provides a valuable tool for the
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experimental study of evolution and ecology.
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Synthetic organisms have been created based on a computer metaphor of
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organic life in which CPU time is the ``energy'' resource and memory is
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the ``material'' resource. Memory is organized into informational
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patterns that exploit CPU time for self-replication. Mutation generates
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new forms, and evolution proceeds by natural selection as different
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genotypes compete for CPU time and memory space. The creatures are
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self-replicating computer programs, however, they can not escape because
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they run exclusively on a virtual computer in its unique machine language.
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The virtual computer is effectively a containment facility.
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A single rudimentary ancestral ``creature'' has been designed; it is 80
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machine instructions long and contains only the code for self-replication.
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This creature examines itself, determines its size and location in the
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memory ``soup'', and then copies itself, one instruction at a time, to
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another location in the soup. The ancestral creature does not interact
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directly with other individuals, although there is scrambling competition
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for access to memory space.
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A reaper kills creatures, assuring that there is always free space into which
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creatures can reproduce. When creatures are born, they enter the bottom of
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the reaper queue, and the reaper always kills off the top, which is usually
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the oldest creature. However, mutant creatures often generate errors, which
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cause them to rise in the reaper queue and be killed.
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>From a single rudimentary ancestral ``creature'' there have evolved tens of
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thousands of self-replicating genotypes of many hundreds of genome size
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classes. Bit flipping mutations cause changes in the sequence of instructions
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in the genome, but they do not cause changes in the size of the genome.
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However, mutant genotypes make errors in their self-examination and
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replication, resulting in different sized genomes. As genetic change
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generates new genotypes, variants appear which are able to replicate more
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rapidly that their ancestors, and those variants increase in frequency in
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the soup.
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Very quickly there evolve parasites, which are not able to replicate in
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isolation because they lack a large portion of the genome. However, these
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parasites search for the missing information, and if they locate it in a
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nearby creature, they parasitize the information from the neighboring genome,
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thereby effecting their own replication. This informational parasitism is
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a commensal relationship, as it is not directly detrimental to the host.
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However, the parasites do compete with the hosts for space, and may be
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superior competitors because they can more rapidly replicate their smaller
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genome. However, their advantage is frequency dependent. As the parasites
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increase in frequency, the hosts decline, and many parasites fail to locate
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hosts. In ecological runs, without genetic change, hosts and parasites
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demonstrate Lotka-Volterra cycles.
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In some runs, hosts evolve immunity to attack by parasites. One immune
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mechanism that has been worked out is based on the fact that the creatures
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only examine themselves once, and rely on retaining the information on their
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size and location for all subsequent replications. Immune hosts cause their
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parasites to loose their sense of self by failing to retain the information
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on size and location. Immune hosts function with this forgetful code by
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re-examining themselves before each repliction, thus there is a metabolic
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cost to the immunity.
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When immune hosts appear, they often increase in frequency, devastating the
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parasite populations. In some runs where the community comes to be
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dominated by immune hosts, parasites evolve that are resistant to immunity.
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The above mentioned immune mechanism can by circumvented by parasites which
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also re-examine themselves before each replication.
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Hosts sometimes evolve a response to parasites that goes beyond immunity to
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actual hyper-parasitism. Hyper-parasites allow themselves to be parasitized,
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letting the parasite use their code for a single replication. After the
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first replication, the hyper-parasite deceives the parasite by replacing the
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parasite's record of its size and location with the size and location of the
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hyper-parasite genome. Thereafter, the parasite will devote its energetic
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resources to replication of the hyper-parastie genome. This is a highly
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deleterious interaction, which drives the parasites to extinction. The
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hyper-parasites are facultative, getting an energy boost when the parasites
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are present, but not requiring them for replication.
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Evolving in the absence of parasites, hyper-parasites completely dominate
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the community, resulting in a relatively uniform community characterize by
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a high degree of relationship between individuals. Under these circumstances,
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sociality evolves, in the sense that the creatures evolve into forms which
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can not replicate in isolation, but which can only replicate in aggregations.
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These colonial creatures cooperate in the control of the flow of execution of
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their algorithms.
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The cooperative behavior of the social hyper-parasites makes them vulnerable
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to a new class of parasites. These cheaters, hyper-hyper-parasites, insert
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themselves between cooperating social individuals, and momentarily seize
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control of execution of the algroithm, just long enough to deceive the
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social creatures about their size and location, causing the social creatures
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to replicate the genomes of the cheaters.
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In a separate experiment, two versions of the ancestral creature were made,
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each with a different portion of the genome deleted. Neither of these
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genomes were able to replicate in isolation. However, when cultured together,
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they each parasitize the missing code from the other, forming an ecologically
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stable obligate symbiotic relationship. When genetic change is allowed in
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the system, a very complex series of changes follows, ultimately resulting
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in the merging of the two genomes into a single self-replicating genome.
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The only kind of genetic change that the simulator imposes on the system is
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random bit flips in the machine code of the creatures. However, it turns
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out that parasites are very sloppy replicators. They cause significant
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recombination and rearrangement of the genomes. This spontaneous sexuality
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is a powerful force for evolutionary change in the system.
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A series of experiments were conducted on the effects of mutation rates on
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the rates of evolution. The parameter used to compare rates of evolution
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was the rate at which self-replicating genomes decreased in size, indicating
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an optimization, in an environment favoring smaller sizes. The optimal
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mutation rate was found to be a mutation affecting one in four individuals
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per generation. At higher rates the community sometimes died out, as genomes
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melted under the mutational heat. At lower rates, optimization was slower.
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Fully self-replicating (non-parasitic) genomes reduced from 80 instructions
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to 22 instructions overnight (more than 1500 generations, of populations
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ranging from 300 to 1000 individuals). The ancestor of size 80 requires
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839 CPU cycles to replicate. The creature of size 22 requires 146 CPU cycles
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to replicate, a 5.75-fold difference in efficiency.
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One of the most interesting aspects of this second instance of life is
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that the bulk of the evolution is based on adaptation to the biotic
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environment rather than the physical environment. It is co-evolution
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that drives the system.
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It is possible to extract information on any aspect of the system
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without disturbing it, from phylogeny or community structure through time
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to the ``genetic makeup'' and ``metabolic processes'' of individuals.
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Synthetic Life demonstrates the power of the computational approach to
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science as a complement to the traditional approaches of experiment and
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theory based on analysis through calculus and differential equations.
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I will make an oral presentation. I will need an overhead projector, and
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two three-pronged power outlets nearby to plug in the computer and LCD panel.
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---------------------------end abstract----------------------------------
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This message was distributed internally to the University of Delaware
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Synthetic Life group. I thought that other AL fans might be interested
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to know what we are up to:
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We haven't met as a group for some time, so I thought I would send out
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this progress report.
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TECHNOLOGY REVIEW ARTICLE - The next issue of Technology Review (April/May),
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due out in March, will include an article on Artificial Life. They
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will describe (among other things) the work of our group, and will include
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a series of four color photos of the ALmond Monitor of Tierra that Marc Cygnus
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has developed.
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ALMOND TALKS - Marc Cygnus has got the ALmond monitor program talking to
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the Tierra simulator using network communications. We can now have multiple
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simulators running on multiple machines, and monitor them from multiple
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monitors on multiple machines. The monitors can attach to and detach from
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the simulators without disturbing them.
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AL AND GA - Chris Bryden has completed his term paper discussing the
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relationships between synthetic life and genetic algorithms.
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THE MATRIX OF LIFE - John Billon has completed his independent study by
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exploring the possibility of implementing a synthetic life system in a
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matrix based environment.
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THE GENETIC LANGUAGE - Dan Pirone has designed a much more powerful version
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of the Tierran language, and has the bulk of the new instruction set coded.
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The syntax is much more complex than the original Tierran. We are hoping that
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it will be as evolvable.
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OPTIMIZATION OF TIERRA - Tom Uffner has tackled the task of optimizing the
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tierra simulator code. He is starting with the genebank manager which works,
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but is very inefficient. His proposals for optimization sound very promising.
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DIVERSITY AND TURNOVER - Eric Andrews and Jim Timmons are developing code to
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monitor diversity and turnover rates of size classes and genotypes in the
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soups. They are already generating the diversity indices, and are working
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on the turnover rates.
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AUTECOLOGY - Over winter session I automated the analysis of ecological
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interactions between creatures. Now when genotypes are saved to disk, the
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code that is actually executed is marked, to distinguish it from "junk"
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(unexecuted) code. Also, the basic classes of ecological interactions have
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been identified, and the interactions engaged in by a genotype are marked in
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a bit field that is saved with each genotype.
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EVOLUTIONARY OPTIMIZATION OF MACHINE CODES - I completed a study of the effect
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of mutation rate on the rate of evolution. As an index of the rate of
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evolution, I used the rate at which self-replicating machine code programs
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reduce their size. The optimal mutation rate was one that hit about one in
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four programs per generation. At higher rates, the communities sometimes
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died out, as genomes melted under the mutational heat. The programs reduced
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themselves from 80 machine instructions to 22 machine instructions overnight
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(over 1500 generations, of populations ranging from 300 to 1000 individuals).
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There was a 5.75-fold decrease in the number of CPU cycles required for
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replication.
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IRISVILLE OPENS - The two Silicon Graphics machines and the Sun in 114 Wolf
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are up and running and on the net. life.slhs.udel.edu is a 4D25TG Personal
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Iris with 32MB of memory and a 1.2 GB disk. tierra.slhs.udel.edu is a
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4D258 (Iris) Data Station Server with 32MB of memory and a 1.2 GB disk.
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genie.slhs.udel.edu is a Sun 3/60 with 8MB of memory, about 300 MB of disk,
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and a color monitor. The Irises are rated at 16 MIPS each, and the Sun at
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about 4 MIPS. These machines are for the exclusive use of the School of
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Life and Health Sciences (SLHS), which so far has meant just for the alife
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group. The two Irises have been running the Tierra simulator around the
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clock since they came up.
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Tom Ray
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University of Delaware
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School of Life & Health Sciences
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Newark, Delaware 19716
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ray@brahms.udel.edu
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302-451-2281 (FAX)
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302-451-2753
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