TIERRA - Evolution Simulator Announcement
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We are releasing the source now, although it is under rapid development and major improvements are only days away. If you use the software, be sure to pick up new versions soon from the ftp site. The source in the ftp site will be replace on a roughly weekly basis.
The complete source code for the Tierra simulator is available by anonymous ftp at:
tierra.slhs.udel.edu [18.104.22.168] andin the directory /tierra.
To get it, ftp to tierra or life, log in as user "anonymous" and give your real name (eg. email@example.com) as a password. Then give the command `cd tierra', to get a list of files type `dir'. You will see the following files:
The shar files contain the README files, so if you want the source code, you don't need to copy README separately. To unpack the shar files, use `unshar', or `sh'.README.T1 A detailed description of tierra and how to use it. README.T2 in two parts Part01 the source code in shar format ... in seven parts Part07 announce this announcement tierra1.tex Parts 1 & 2 of a manuscript describing Tierra, tierra2.tex in LaTeX format.
The version released includes significant contributions from Tom Uffner, Dan Pirone and Marc Cygnus. The software remains copyrighted ("all rights reserved"), and is not being placed in the public domain. However, it will be made available free of charge and may be freely distributed. The intent is that it not be used for profit making activities unless some royalty arrangement is entered into with the authors.
A DOS version of the Tierra software with a decent frontend will be ready for sale ($70) by November.
Synthetic organisms have been created 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 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.
Observation of nature shows that evolution by natural selection is capable of both optimization and creativity. Artificial models of evolution have demonstrated the optimizing ability of evolution, as exemplified by the field of genetic algorithms. The creative aspects of evolution have been more elusive to model. The difficulty derives in part from a tendency of models to specify the meaning of the ``genome'' of the evolving entities, precluding new meanings from emerging. I will present a natural model of evolution demonstrating both optimization and creativity, in which the genome consists of sequences of executable machine code.
From a single rudimentary ancestral ``creature'', very quickly there evolve parasites, which are not able to replicate in isolation because they lack a large portion of the genome. However, these parasites search for the missing information, and if they locate it in a nearby creature, parasitize the information from the neighboring genome, thereby effecting their own replication.
In some runs, hosts evolve immunity to attack by parasites. When immune hosts appear, they often increase in frequency, devastating the parasite populations. In some runs where the community comes to be dominated by immune hosts, parasites evolve that are resistant to immunity.
Hosts sometimes evolve a response to parasites that goes beyond immunity, to actual (facultative) hyper-parasitism. The hyper-parasite deceives the parasite causing the parasite to devote its energetic resources to replication of the hyper-parastie genome. This drives the parasites to extinction.
Evolving in the absence of parasites, hyper-parasites completely dominate the community, resulting in a relatively uniform community characterize by a high degree of relationship between individuals. Under these circumstances, sociality evolves, in the form of creatures which can only replicate in aggregations.
The cooperative behavior of the social hyper-parasites makes them vulnerable to a new class of parasites. These cheaters, hyper-hyper-parasites, insert themselves between cooperating social individuals, deceiving the social creatures, causing them to replicate the genomes of the cheaters.
The only genetic change imposed on the simulator is random bit flips in the machine code of the creatures. However, it turns out that parasites are very sloppy replicators. They cause significant recombination and rearrangement of the genomes. This spontaneous sexuality is a powerful force for evolutionary change in the system.
One of the most interesting aspects of this instance of life is that the bulk of the evolution is based on adaptation to the biotic environment rather than the physical environment. It is co-evolution that drives the system.
The recent publicity about my work (Technology Review, April 1991; Science News, August 10, 1991; New York Times, August 27, 1991; Computerworld September 30, 1991) has generated a lot of interest. I wanted to list the relevant publications, and also the upcoming seminars.
Ray, T. S. 1991. ``Is it alive, or is it GA?'' Proceedings of the 1991 International Conference on Genetic Algorithms, Eds. Belew, R. K., and L. B. Booker, San Mateo, CA: Morgan Kaufmann, 527--534.
Ray, T. S. 1991. ``An approach to the synthesis of life.'' Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, vol. XI, Eds. C. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen, Redwood City, CA: Addison-Wesley, 371--408.
Ray, T. S. 1991. ``Population dynamics of digital organisms.'' Artificial Life II Video Proceedings, Ed. C. G. Langton, Redwood City, CA: Addison Wesley.
Ray, T. S. 1991. ``Evolution and optimization of digital organisms.'' Scientific Excellence in Supercomputing: The IBM 1990 Contest Prize Papers, Eds. Keith R. Billingsley, Ed Derohanes, Hilton Brown, III. Athens, GA, 30602, The Baldwin Press, The University of Georgia. Publication date: December 1991.
I will be at the Santa Fe Institute Feb. 1 thru Aug. 31, 1992. This work will also be presented in the following upcoming seminars:
University of Maryland, Zoology, October 29, 1991
University of Kentucky, Lexington, Biology, October 31, 1991
University of Delaware, Entomology, November 5, 1991
Stony Brook, Department of Ecology and Evolution, November 6, 1991
Drexel University, Electrical Engineering, November 8, 1991
The University of the Arts, Philadelphia, Design in Cyberspace lectures, November 12, 1991
IBM, T. J. Watson Research Center, Yorktown Heights, NY, November 13, 1991
Thinking Machines Corp., Cambridge, November 14, 1991
Digital Equipment Corp., Hudson, MA, November 15, 1991
American Society of Information Science, New Jersey, November 19, 1991
Texas Instruments, Dallas, November 21, 1991
Harvard University, Biology (Lewontin's lab), December 2, 1991
Boston University, Computational Sciences Center, December 3, 1991
MIT Nanotechnology Study Group, December 3, 1991
University of Massachusetts Boston, Biology, December 5, 1991
Yale University, Biology, December 6, 1991
University of Arizona, Ecology & Evolutionary Biology, March 10, 1992
Cornell University, Mathematical Sciences Institute, CA Workshop, May 1992
Gordon Conference on Theoretical Biology, New Hampshire, June 8--12, 1992
Below is a report of an interesting result that is not described in any of the publications listed above: A Complex Adaptation
The adaptation described below is a classic example of intricate design in evolution. One wonders how it could have arisen through random bit flips, as every component of the code must be in place in order for the algorithm to function. Yet the code includes a classic mix of apparent intelligent design, and the chaotic hand of evolution. The optimization technique is a very clever one invented by humans, yet it is implemented in a mixed up but functional style that no human would use (unless perhaps very intoxicated).
The arms race described in the manuscripts took place over a period of a billion instructions executed by the system. Another run was allowed to continue for fifteen billion instructions, but was not examined in detail. A creature present at the end of the run was examined and found to have evolved an intricate adaptation. The adaptation is an optimization technique known as ``unrolling the loop''.
The central loop of the copy procedure performs the following operations: 1) copies an instruction from the mother to the daughter, 2) decrements the cx register which initially contains the size of the parent genome, 3) tests to see if cx is equal to zero, if so it exits the loop, if not it remains in the loop, 4) increments the ax register which contains the address in the daughter where the next instruction will be copied to, 5) increments the bx register which contains the address in the mother where the next instruction will be copied from, 6) jumps back to the top of the loop.
The work of the loop is contained in steps 1, 2, 4 and 5. Steps 3 and 6 are overhead. The efficiency of the loop can be increased by duplicating the work steps within the loop, thereby saving on overhead. The creature from the end of the long run had repeated the work steps three times within the loop, as illustrated below.
The unrolled loop is an example of the ability of evolution to produce an increase in complexity, gradually over a long period of time. The interesting thing about the loop unrolling optimization technique is that it requires more complex code. The resulting creature has a genome size of 36, compared to its ancestor of size 80, yet it has packed a much more complex algorithm into less than half the space.
Below I include the assembler code for the central copy loop of the ancestor (80aaa) and decendant after fifteen billion instructions (72etq). Within the loop, the ancestor does each of the following operations once: copy instruction (51), decrement cx (52), increment ax (59) and increment bx (60). The decendant performs each of the following operations three times within the loop: copy instruction (15, 22, 26), increment ax (20, 24, 31) and increment bx (21, 25, 32). The decrement cx operation occurs five times within the loop (16, 17, 19, 23, 27). Instruction 28 flips the low order bit of the cx register. Whenever this latter instruction is reached, the value of the low order bit is one, so this amounts to a sixth instance of decrement cx. This means that there are two decrements for every increment.
The reason for this is related to another adaptation of this creature. When it calculates its size, it shifts left (12) before allocating space for the daughter (13). This has the effect of allocating twice as much space as is actually needed to accomodate the genome. The genome of the creature is 36 instructions long, but it allocates a space of 72 instructions. This occurred in an environment where the slice size was set equal to the size of the cell. In this way the creatures were able to garner twice as much energy. However, they had to compliment this change by doubling the number of decrements in the loop.
nop_1 ; 01 47 copy loop template COPY LOOP OF 80AAA nop_0 ; 00 48 copy loop template nop_1 ; 01 49 copy loop template nop_0 ; 00 50 copy loop template mov_iab ; 1a 51 move contents of [bx] to [ax] (copy instruction) dec_c ; 0a 52 decrement cx if_cz ; 05 53 if cx = 0 perform next instruction, otherwise skip it jmp ; 14 54 jump to template below (copy procedure exit) nop_0 ; 00 55 copy procedure exit compliment nop_1 ; 01 56 copy procedure exit compliment nop_0 ; 00 57 copy procedure exit compliment nop_0 ; 00 58 copy procedure exit compliment inc_a ; 08 59 increment ax (point to next instruction of daughter) inc_b ; 09 60 increment bx (point to next instruction of mother) jmp ; 14 61 jump to template below (copy loop) nop_0 ; 00 62 copy loop compliment nop_1 ; 01 63 copy loop compliment nop_0 ; 00 64 copy loop compliment nop_1 ; 01 65 copy loop compliment (10 instructions executed per loop) shl ; 000 03 12 shift left cx COPY LOOP OF 72ETQ mal ; 000 1e 13 allocate daughter cell nop_0 ; 000 00 14 top of loop mov_iab ; 000 1a 15 copy instruction dec_c ; 000 0a 16 decrement cx dec_c ; 000 0a 17 decrement cx jmpb ; 000 15 18 junk dec_c ; 000 0a 19 decrement cx inc_a ; 000 08 20 increment ax inc_b ; 000 09 21 increment bx mov_iab ; 000 1a 22 copy instruction dec_c ; 000 0a 23 decrement cx inc_a ; 000 08 24 increment ax inc_b ; 000 09 25 increment bx mov_iab ; 000 1a 26 copy instruction dec_c ; 000 0a 27 decrement cx or1 ; 000 02 28 flip low order bit of cx if_cz ; 000 05 29 if cx == 0 do next instruction ret ; 000 17 30 exit loop inc_a ; 000 08 31 increment ax inc_b ; 000 09 32 increment bx jmpb ; 000 15 33 go to top of loop (6 instructions per copy) nop_1 ; 000 01 34 bottom of loop (18 instructions executed per loop)
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