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O / ================================= x ------------------------------------- o \ \LP \bf 3.3 ECOLOGY\rm \eLP The only communities whose ecology has been explored in detail are those that operate under selection for small sizes. These communities generally include a large number of parasites, which do not have functional copy procedures, and which execute the copy procedures of other creatures within the search limit. In exploring ecological interactions, the mutation rate is set at zero, which effectively throws the simulation into ecological time by stopping evolution. When parasites are present, it is also necessary to stipulate that creatures must breed true, since parasites have a tendency to scramble genomes, leading to evolution in the absence of mutation. 0045aaa is a ``metabolic parasite''. Its genome does not include the copy procedure, however it executes the copy procedure code of a normal host, such as the ancestor. In an environment favoring small creatures, 0045aaa has a competitive advantage over the ancestor, however, the relationship is density dependent. When the hosts become scarce, most of the parasites are not within the search limit of a copy procedure, and are not able to reproduce. Their calls to the copy procedure fail and generate errors, causing them to rise to the top of the reaper queue and die. When the parasites die off, the host population rebounds. Hosts and parasites cultured together demonstrate Lotka-Volterra population cycling (Lotka [25]; Volterra [35]; Wilson \& Bossert [36]). A number of experiments have been conducted to explore the factors affecting diversity of size classes in these communities. Competitive exclusion trials were conducted with a series of self-replicating (non-parasitic) genotypes of different size classes. The experimental soups were initially inoculated with one individual of each size. A genotype of size 79 was tested against a genotype of size 80, and then against successively larger size classes. The interactions were observed by plotting the population of the size 79 class on the $x$ axis, and the population of the other size class on the $y$ axis. Sizes 79 and 80 were found to be competitively matched such that neither was eliminated from the soup. They quickly entered a stable cycle, which exactly repeated a small orbit. The same general pattern was found in the interaction between sizes 79 and 81. When size 79 was tested against size 82, they initially entered a stable cycle, but after about 4 million instructions they shook out of stability and the trajectory became chaotic with an attractor that was symmetric about the diagonal (neither size showed any advantage). This pattern was repeated for the next several size classes, until size 90, where a marked asymmetry of the chaotic attractor was evident, favoring size 79. The run of size 79 against size 93 showed a brief stable period of about a million instructions, which then moved to a chaotic phase without an attractor, which spiraled slowly down until size 93 became extinct, after an elapsed time of about 6 million instructions. An interesting exception to this pattern was the interaction between size 79 and size 89. Size 89 is considered to be a ``metabolic cripple'', because although it is capable of self-replicating, it executes about 40\% more instructions than normal to replicate. It was eliminated in competition with size 79, with no loops in the trajectory, after an elapsed time of under one million instructions. In an experiment to determine the effects of the presence of parasites on community diversity, a community consisting of twenty size classes of hosts was created and allowed to run for 30 million instructions, at which time only the eight smallest size classes remained. The same community was then regenerated, but a single genotype (0045aaa) of parasite was also introduced. After 30 million instructions, 16 size classes remained, including the parasite. This seems to be an example of a ``keystone'' parasite effect (Paine [28]). Symbiotic relationships are also possible. The ancestor was manually dissected into two creatures, one of size 46 which contained only the code for self-examination and the copy loop, and one of size 64 which contained only the code for self-examination and the copy procedure (Figure 3). Neither could replicate when cultured alone, but when cultured together, they both replicated, forming a stable mutualistic relationship. It is not known if such relationships have evolved spontaneously. \LP \bf 3.4 EVOLUTIONARY OPTIMIZATION\rm \eLP In order to compare the process of evolution between runs of the simulator, a simple objective quantitative measure of evolution is needed. One such measure is the degree to which creatures improve their efficiency through evolution. This provides not only an objective measure of progress in evolution, but also sheds light on the potential application of synthetic life systems to the problem of the optimization of machine code. The efficiency of the creature can be indexed in two ways: the size of the genome, and the number of CPU cycles needed to execute one replication. Clearly, smaller genomes can be replicated with less CPU time, however, during evolution, creatures also decrease the ratio of instructions executed in one replication, to genome size. The number of instructions executed per instruction copied, drops substantially. Figure 4 shows the changes in genome size over a time period of 500 million instructions executed by the system, for eight sets of mutation rates differing by factors of two. Mutation rates are measured in terms of 1 in N individuals being affected by a mutation in each generation. At the highest two sets of rates tested, one and two, either each (one) or one-half (two) of the individuals are hit by mutation in each generation. At these rates the system is unstable. The genomes melt under the heat of the high mutation rates. The community often dies out, although some runs survived the 500 million instruction runs used in this study. The next lower rate, four, yields the highest rate of optimization without the risk of death of the community. At the five lower mutation rates, 8, 16, 32, 64 and 128, we see successively lower rates of optimization. Additional replicates were made of the runs at the mutation rate of four (Fig.\ 5). The replicates differ only in the seed of the random number generator, all other parameters being identical. These runs vary in some details such as whether progress is continuous and gradual, or comes in bursts. Also, each run decreases to a size limit which it can not proceed past even if it is allowed to run much longer. However, different runs reach different plateaus of efficiency. The smallest limiting genome size seen has been 22 instructions, while other runs reached limits of 27 and 30 instructions. Evidently, the system can reach a local optima from which it can not easily evolve to the global optima. The increase in efficiency of the replicating algorithms is even greater than the decrease in the size of the code. The ancestor is 80 instructions long and requires 839 CPU cycles to replicate. The creature of size 22 only requires 146 CPU cycles to replicate, a 5.75--fold difference in efficiency. The algorithm of one of these creatures is listed in Appendix D. Although optimization of the algorithm is maximized at the highest mutation rate that does not cause instability, ecological interactions appear to be richer at slightly lower mutation rates. At the rates of eight or 16, we find the diversity of coexisting size classes to be the greatest, and to persist the longest. The smaller size classes tend to be various forms of parasites, thus a diversity of size classes indicates a rich ecology. An example of even greater optimization is illustrated in Appendix E and discussed above in section 3.1.1.8. Unrolling of the loop results in a loop which uses 18 CPU cycles to copy three instructions, or six CPU cycles executed per instruction copied, compared to 10 for the ancestor. The creature of size 22 also uses six CPU cycles per instruction copied. However, the creature of Appendix E uses three extra CPU cycles per loop to compensate for a separate adaptation that allows it to double its share of CPU time from the global pool (in essence meaning that relatively speaking, it uses only three CPU cycles per instruction copied). Without this compensation it would use only five CPU cycles per instruction copied. \LP \rule[6pt]{6.5in}{1pt} \large \bf 4 SUMMARY\rm \normalsize \eLP \LP \bf 4.1 GENERAL BEHAVIOR OF THE SYSTEM\rm \eLP Once the soup is full of replicating creatures, individuals are initially short lived, generally reproducing only once before dying, thus individuals turn over very rapidly. More slowly, there appear new genotypes of size 80, and then new size classes. There are changes in the genetic composition of each size class, as new mutants appear, some of which increase significantly in frequency, eventually replacing the original genotype. The size classes which dominate the community also change through time, as new size classes appear, some of which competitively exclude sizes present earlier. Once the community becomes diverse, there is a greater variance in the longevity and fecundity of individuals. In addition to an increase in the raw diversity of genotypes and genome sizes, there is an increase in the ecological diversity. Obligate commensal parasites evolve, which are not capable of self-replication in isolated culture, but which can replicate when cultured with normal (self-replicating) creatures. These parasites execute some parts of the code of their hosts, but cause them no direct harm, except as competitors. Some potential hosts have evolved immunity to the parasites, and some parasites have evolved to circumvent this immunity. In addition, facultative hyper-parasites have evolved, which can self-replicate in isolated culture, but when subjected to parasitism, subvert the parasites energy metabolism to augment their own reproduction. Hyper-parasites drive parasites to extinction, resulting in complete domination of the communities. The relatively high degrees of genetic relatedness within the hyper-parasite dominated communities leads to the evolution of sociality in the sense of creatures that can only replicate when they occur in aggregations. These social aggregations are then invaded by hyper-hyper-parasite cheaters. Mutations and the ensuing replication errors lead to an increasing diversity of sizes and genotypes of self-replicating creatures in the soup. Within the first 100 million instructions of elapsed time, the soup evolves to a state in which about a dozen more-or-less persistent size classes coexist. The relative abundances and specific list of the size classes varies over time. Each size class consists of a number of distinct genotypes which also vary over time. The rate of evolution increases with the mutation rate until the system becomes unstable, and the community dies at rates above one mutation per four generations. Ecological interactions are richer and more sustained at slightly lower rates, one mutation per eight or 16 generations. At mutation rates of one per four generations, under selection for small sizes, creatures will optimize to a genome size in the 22 to 30 instruction size range within as little as 300 million instructions of elapsed time. Each of these runs will reach a local optima which it evidently cannot escape from, although it may not be the global optima. \LP \bf 4.2 INCREASING COMPLEXITY\rm \eLP The unrolled loop (section 3.1.1.8) 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 (Appendix E). This 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). \LP \bf 4.3 EMERGENCE\rm \eLP The ``physical'' environment presented by the simulator is quite simple, consisting of the energy resource (CPU time) doled out rather uniformly by the time slicer, and memory space which is completely uniform and always available. In light of the nature of the physical environment, the implicit fitness function would presumably favor the evolution of creatures which are able to replicate with less CPU time, and this does in fact occur. However, much of the evolution in the system consists of the creatures discovering ways to exploit one-another. The creatures invent their own fitness functions through adaptation to their biotic (``living'') environment. These ecological interactions are not programmed into the system, but emerge spontaneously as the creatures discover each other and invent their own games. In the Tierran world, creatures which initially do not interact, discover means to exploit one another, and in response, means to avoid exploitation. The original fitness landscape of the ancestor consists only of the efficiency parameters of the replication algorithm, in the context of the properties of the reaper and slicer queues. When by chance, genotypes appear that exploit other creatures, selection acts to perfect the mechanisms of exploitation, and mechanisms of defense to that exploitation. The original fitness landscape was based only on adaptations of the organism to its physical environment. The new fitness landscape retains those features, but adds to it adaptations to the biotic environment, the other creatures. Because the fitness landscape includes an ever increasing realm of adaptations to other creatures which are themselves evolving, it can facilitate an auto-catalytic increase in complexity and diversity of organisms. Evolutionary theory suggests that adaptation to the biotic environment (other organisms) rather than to the physical environment is the primary force driving the auto-catalytic diversification of organisms (Stanley [34]). It is encouraging to discover that the process has already begun in the Tierran world. It is worth noting that the results presented here are based on evolution of the first creature that I designed, written in the first instruction set that I designed. Comparison to the creatures that have evolved shows that the one I designed is not a particularly clever one. Also, the instruction set that the creatures are based on is certainly not very powerful (apart from those special features incorporated to enhance its evolvability). It would appear then that it is rather easy to create life. Evidently, virtual life is out there, waiting for us to provide environments in which it may evolve. \LP \bf 4.4 SYNTHETIC BIOLOGY\rm \eLP One of the most uncanny of evolutionary phenomena is the ecological convergence of biota living on different continents or in different epochs. When a lineage of organisms undergoes an adaptive radiation (diversification), it leads to an array of relatively stable ecological forms. The specific ecological forms are often recognizable from lineage to lineage. For example among dinosaurs, the \it Pterosaur\rm, \it Triceratops\rm, \it Tyrannosaurus \rm and \it Ichthyosaur \rm are ecological parallels respectively, to the bat, rhinoceros, lion and porpoise of modern mammals. Similarly, among modern placental mammals, the gray wolf, flying squirrel, great anteater and common mole are ecological parallels respectively, to the Tasmanian wolf, honey glider, banded anteater and marsupial mole of the marsupial mammals of Australia. Given these evidently powerful convergent forces, it should perhaps not be surprising that as adaptive radiations proceed among digital organisms, we encounter recognizable ecological forms, in spite of the fundamentally distinct physics and chemistry on which they are based. Ideally, comparisons should be made among organisms of comparable complexity. It may not be appropriate to compare viruses to mammals. Unfortunately, the organic creatures most comparable to digital organisms, the RNA creatures, are no longer with us. Since digital organisms are being compared to modern organic creatures of much greater complexity, ecological comparisons must be made in the broadest of terms. Trained biologists will tend to view synthetic life in the same terms that they have come to know organic life. Having been trained as an ecologist and evolutionist, I have seen in my synthetic communities, many of the ecological and evolutionary properties that are well known from natural communities. Biologists trained in other specialties will likely observe other familiar properties. It seems that what we see is what we know. It is likely to take longer before we appreciate the unique properties of these new life forms. \LP \bf 4.5 FUTURE DIRECTIONS\rm \eLP For the immediate future, my research will involve three main thrusts: \bf Computational: \rm The computational issue is how to design a computer architecutre and operating system that will support the natural evolution of \it machine code\rm . Von Neumann style machine codes are considered to be brittle, in the sense that they are not robust to the genetic operations of mutation and recombination. Any random change in a program is almost 100\% certain to break the program. The most significant accomplishment of my work to date is finding a way to overcome this brittleness with only a slight modification of standard machine codes. However, this success was achieved in the first attempt. Therefore it is not known what features are essential for evolvability, and I certainly do not know what is the optimal architecture. The primary computational objective then is to experiment with a large number of variations on the successful architecture in order to find the optimal balance of computational power and evolvability. This work has begun but needs to be scaled up. There are however, other important computational goals. We must experiment with artificial selection on application programs. So far all work has involved natural selection on ``wild'' algorithms that do no useful work. I want to develop an analog to genetic engineering, in which application codes are inserted into the genomes of digital organisms and evolved to greater optimality or new functionality. It should be possible to develop cross-assemblers between Tierran architectures and real assembler languages. Application code written and compiled to run on real machines could be cross-assembled into the new Tierran languages. Each procedure could then be inserted into the genome of a creature. Creatures could be rewarded with CPU time in proportion to the efficacy and efficiency of the evolving inserted code. In this way, artificial selection would lead to the optimization of the inserted code, which could then be cross-assembled back into the real machine code. If artificial selection of application programs proved to be practical, it would be worthwhile to render the best Tierran virtual instruction sets in silicon, thereby greatly accelerating the process. At present, maximum optimization can be achieved in a few hours of running the Tierran virtual computer. If a real computer were based on the architectural principals of the Tierran computer, the speed would be multiplied by about two orders of magnitude. If the real machine were massively parallel, there could be additional gains of five to six orders of magnitude. If machine code could evolve that quickly, then there is the possibility of using it as a generative process in addition to an optimization procedure. There may also be some potential application in the areas of machine learning or adaptive programming. Another long term objective is to use digital organisms evolving freely or under artificial selection, as a source of new paradigms for the programming of massively parallel machines. The virtual computer that supports digital evolution is a parallel machine of the MIMD (multiple instruction multiple data) type. One of the biggest problems facing computer science today is the development of techniques of parallel programming. Digital organisms program themselves, using evolution. They have discovered on their own, known programming techniques such as unrolling loops. They will discover techniques that are naturally efficient on parallel machines, and we should be able to learn from their innovations. The kinds of ecological interactions already observed in digital communities could in another light, be viewed as optimization techniques for parallel programming (e.g., the sharing of code fragments). However, these interactions evolve in a ``jungle''-like environment where most interactions are of an adversarial nature. When evolving large parallel application programs, the most viable model would be a multi-cellular one, where many cells would cooperate on a common problem. A multi-cellular model is under development. In the end, evolution may prove to be the best method of programming massively parallel machines. \bf Biological: \rm I plan to move the biological model ahead of its present state. This will primarly involve the incorporation of facilities to support diploidy, organized sexuality, and multi-cellularity. The methods for these advances have already been conceptually worked out, and the implementation has begun. When these improvements are made, the long term biological goals are to use the model to test ecological and evolutionary theory, in such areas as: the evolution of sex, selection across hierarchical levels, and factors affecting diversity of ecological communities. It is hoped that it will be possible to engineer the system up to a condition analagous to the threshold of the Cambrian explosion of diversity, and then just allow the complexity and diversity of the digital system to explode spontaneously. \bf Educational: \rm I wish to distribute both source and executables for use as an educational and research tool. However, some additional work is needed to make the program fully portable and to provide a user friendly graphic user interface. This work is underway at a slow pace. \LP \rule[6pt]{6.5in}{1pt} \large \bf 5 ACKNOWLEDGMENT\rm \normalsize \eLP I thank Marc Cygnus, Robert Eisenberg, Doyne Farmer, Walter Fontana, Stephanie Forrest, Chris Langton, Dan Pirone, Stephen Pope, and Steen Rasmussen, for their discussions or readings of the manuscripts. I thank the Santa Fe Institute for their support. Contribution No.\ XXX from the Ecology Program, School of Life and Health Sciences, University of Delaware. \newpage \LP \rule[6pt]{6.5in}{1pt} \large \bf 6 REFERENCES\rm \normalsize \eLP \XP [1] Ackley, D. H. \& Littman, M. S. ``Learning from natural selection in an artificial environment.'' In: \it Proceedings of the International Joint Conference on Neural Networks, Volume I, Theory Track, Neural and Cognitive Sciences Track\rm , IJCNN Winter 1990, Washington, DC. Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1990. [2] Aho, A. V., Hopcroft, J. E. \& Ullman, J. D. \it The design and analysis of computer algorithms\rm . Reading, Mass.: Addison-Wesley Publ.\ Co, 1974. [3] Bagley, R. J., Farmer, J. D., Kauffman, S. A., Packard, N. H., Perelson, A. S. \& Stadnyk, I. M. ``Modeling adaptive biological systems.'' Unpublished paper, 1989. [4] Barbieri, M. \it The semantic theory of evolution\rm . London: Harwood Academic Publishers, 1985. 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D. \& Belin, A. ``Artificial life: the coming evolution.'' Proceedings in celebration of Murray Gell-Man's 60th Birthday. Cambridge: University Press. In press. [18] Gould, S. J. \it Wonderful life, the Burgess shale and the nature of history\rm . New York: W. W. Norton \& Company, 1989. [19] Gould, S. J. \& Eldredge, N. ``Punctuated equilibria: the tempo and mode of evolution reconsidered.'' \it Paleobiology \bf 3 \rm (1977): 115--151. [20] Holland, J. H. \it Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence\rm . Ann Arbor: Univ.\ of Michigan Press, 1975. [21] Holland, J. H. ``Studies of the spontaneous emergence of self-replicating systems using cellular automata and formal grammars.'' In: \it Automata, Languages, Development\rm , edited by Lindenmayer, A., \& Rozenberg, G. New York: North-Holland, 1976, 385--404. [22] Langton, C. G. ``Studying artificial life with cellular automata.'' \it Physica \bf 22D \rm (1986): 120--149. [23] Langton, C. G. ``Virtual state machines in cellular automata.'' \it Complex Systems \bf 1 \rm (1987): 257--271. [24] Langton, C. G. ``Artificial life.'' In: \it Artificial life: proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems\rm , edited by Langton, C. Vol. 6 in the series: Santa Fe Institute studies in the sciences of complexity. Redwood City, CA: Addison-Wesley, 1989, 1--47. [25] Lotka, A. J. \it Elements of physical biology\rm . Baltimore: Williams and Wilkins, 1925, reprinted as \it Elements of mathematical biology\rm , Dover Press, 1956. [26] Minsky, M. L. \it Computation: finite and infinite machines\rm . Englewood Cliffs, N.J.: Prentice-Hall, 1976. [27] Morris, S. C. ``Burgess shale faunas and the cambrian explosion.'' \it Science \bf 246 \rm (1989): 339--346. [28] Paine, R. T. ``Food web complexity and species diversity.'' \it Am.\ Nat.\ \bf 100 \rm (1966): 65--75. [29] Packard, N. H. ``Intrinsic adaptation in a simple model for evolution.'' In: \it Artificial life: proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems\rm , edited by C. Langton. Redwood City, CA: Addison-Wesley, 1989, 141--155. [30] Pattee, H. H. ``Simulations, realizations, and theories of life.'' In: \it Artificial life: proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems\rm , edited by C. Langton. Redwood City, CA: Addison-Wesley, 1989, 63--77. [31] Rasmussen, S., Knudsen, C., Feldberg, R. \& Hindsholm, M. ``The coreworld: emergence and evolution of cooperative structures in a computational chemistry'' \it Physica D \bf 42 \rm (1990): 111--134. [32] Rheingold, H. (1988). Computer viruses. \it Whole Earth Review \bf Fall \rm (1988): 106. [33] Spafford, E. H., Heaphy, K. A. \& Ferbrache, D. J. \it Computer viruses, dealing with electronic vandalism and programmed threats\rm . ADAPSO, 1300 N. 17th Street, Suite 300, Arlington, VA 22209, 1989. [34] Stanley, S. M. ``An ecological theory for the sudden origin of multicellular life in the late precambrian.'' \it Proc.\ Nat.\ Acad.\ Sci.\ \bf 70 \rm (1973): 1486--1489. [35] Volterra, V. ``Variations and fluctuations of the number of individuals in animal species living together.'' In: \it Animal Ecology\rm , edited by R. N. Chapman. New York: McGraw-Hill, 1926, 409--448. [36] Wilson, E. O. \& Bossert, W. H. \it A primer of population biology\rm . Stamford, Conn: Sinauer Associates, 1971. \eXP \newpage % \addtocounter{page}{1} \LP \bf Figure 1. Metabolic flow chart for the ancestor, parasite, hyper-parasite, and their interactions: \rm ax, bx and cx refer to CPU registers where location and size information are stored. [ax] and [bx] refer to locations in the soup indicated by the values in the ax and bx registers. Patterns such as 1101 are complementary templates used for addressing. Arrows outside of boxes indicate jumps in the flow of execution of the programs. The dotted-line arrows indicate flow of execution between creatures. The parasite lacks the copy procedure, however, if it is within the search limit of the copy procedure of a host, it can locate, call and execute that procedure, thereby obtaining the information needed to complete its replication. The host is not adversely affected by this informational parasitism, except through competition with the parasite, which is a superior competitor. Note that the parasite calls the copy procedure of its host with the expectation that control will return to the parasite when the copy procedure returns. However, the hyper-parasite jumps out of the copy procedure rather than returning, thereby seizing control from the parasite. It then proceeds to reset the CPU registers of the parasite with the location and size of the hyper-parasite, causing the parasite to replicate the hyper-parasite genome thereafter. \newpage \addtocounter{page}{1} \bf Figure 2. Metabolic flow chart for social hyper-parasites, their associated hyper-hyper-parasite cheaters, and their interactions. \rm Symbols are as described for Fig.\ 1. Horizontal dashed lines indicate the boundaries between individual creatures. On both the left and right, above the dashed line at the top of the figure is the lowermost fragment of a social-hyper-parasite. Note (on the left) that neighboring social hyper-parasites cooperate in returning the flow of execution to the beginning of the creature for self-re-examination. Execution jumps back to the end of the creature above, but then falls off the end of the creature without executing any instructions of consequence, and enters the top of the creature below. On the right, a cheater is inserted between the two social-hyper-parasites. The cheater captures control of execution when it passes between the social individuals. It sets the CPU registers with its own location and size, and then skips over the self-examination step when it returns control of execution to the social creature below. \newpage \addtocounter{page}{1} \bf Figure 3. Metabolic flow chart for obligate symbionts and their interactions. \rm Symbols are as described for Fig.\ 1. Neither creature is able to self-replicate in isolation. However, when cultured together, each is able to replicate by using information provided by the other. \newpage \addtocounter{page}{1} \bf Figure 4. Evolutionary optimization at eight sets of mutation rates. \rm In each run, the three mutation rates: move mutations (copy error), flaws and background mutations (cosmic rays) are set relative to the generation time. In each case, the background mutation rate is the lowest, affecting a cell once in twice as many generations as the move mutation rate. The flaw rate is intermediate, affecting a cell once in 1.5 times as many generations as the move mutation rate. For example in one run, the move mutation will affect a cell line on the average once every 4 generations, the flaw will occur once every 6 generations, and the background mutation once every 8 generations. The horizontal axis shows elapsed time in hundreds of millions of instructions executed by the system. The vertical axis shows genome size in instructions. Each point indicates the first appearance of a new genotype which crossed the abundance thresholds of either 2\% of the population of cells in the soup, or occupation of 2\% of the memory. The number of generations per move mutation is indicated by a number in the upper right hand corner of each graph. \newpage \addtocounter{page}{3} \bf Figure 5. Variation in evolutionary optimization under constant conditions. \rm Based on a mutation rate of four generations per move mutation, all other parameters as in Fig.\ 4. The plots are otherwise as described for Fig.\ 4. \newpage \addtocounter{page}{1} Table 1: Genebank. Table of numbers of size classes in the genebank. Left column is size class, right column is number of self-replicating genotypes of that size class. 305 sizes, 29,275 genotypes. \eLP \begin{verbatim} 0034 1 0092 362 0150 2 0205 5 0418 1 5213 2 0041 2 0093 261 0151 1 0207 3 0442 10 5229 4 0043 12 0094 241 0152 2 0208 2 0443 1 5254 1 0044 7 0095 211 0153 1 0209 1 0444 61 5888 36 0045 191 0096 232 0154 2 0210 9 0445 1 5988 1 0046 7 0097 173 0155 3 0211 4 0456 2 6006 2 0047 5 0098 92 0156 77 0212 4 0465 6 6014 1 0048 4 0099 117 0157 270 0213 5 0472 6 6330 1 0049 8 0100 77 0158 938 0214 47 0483 1 6529 1 0050 13 0101 62 0159 836 0218 1 0484 8 6640 1 0051 2 0102 62 0160 3229 0219 1 0485 3 6901 5 0052 11 0103 27 0161 1417 0220 2 0486 9 6971 1 0053 4 0104 25 0162 174 0223 3 0487 2 7158 2 0054 2 0105 28 0163 187 0226 2 0493 2 7293 3 0055 2 0106 19 0164 46 0227 7 0511 2 7331 1 0056 4 0107 3 0165 183 0231 1 0513 1 7422 70 0057 1 0108 8 0166 81 0232 1 0519 1 7458 1 0058 8 0109 2 0167 71 0236 1 0522 6 7460 7 0059 8 0110 8 0168 9 0238 1 0553 1 7488 1 0060 3 0111 71 0169 15 0240 3 0568 6 7598 1 0061 1 0112 19 0170 99 0241 1 0578 1 7627 63 0062 2 0113 10 0171 40 0242 1 0581 3 7695 1 0063 2 0114 3 0172 44 0250 1 0582 1 7733 1 0064 1 0115 3 0173 34 0251 1 0600 1 7768 2 0065 4 0116 5 0174 15 0260 2 0683 1 7860 25 0066 1 0117 3 0175 22 0261 1 0689 1 7912 1 0067 1 0118 1 0176 137 0265 2 0757 6 8082 3 0068 2 0119 3 0177 13 0268 1 0804 2 8340 1 0069 1 0120 2 0178 3 0269 1 0813 1 8366 1 0070 7 0121 60 0179 1 0284 16 0881 6 8405 5 0071 5 0122 9 0180 16 0306 1 0888 1 8406 2 0072 17 0123 3 0181 5 0312 1 0940 2 8649 2 0073 2 0124 11 0182 27 0314 1 1006 6 8750 1 0074 80 0125 6 0184 3 0316 2 1016 1 8951 1 0075 56 0126 11 0185 21 0318 3 1077 5 8978 3 0076 21 0127 1 0186 9 0319 2 1116 1 9011 3 0077 28 0130 3 0187 3 0320 23 1186 1 9507 3 0078 409 0131 2 0188 11 0321 5 1294 7 9564 3 0079 850 0132 5 0190 20 0322 21 1322 7 9612 1 0080 7399 0133 2 0192 12 0330 1 1335 1 9968 1 0081 590 0134 7 0193 4 0342 5 1365 11 10259 31 0082 384 0135 1 0194 4 0343 1 1631 1 10676 1 0083 886 0136 1 0195 11 0351 1 1645 3 11366 5 0084 1672 0137 1 0196 19 0352 3 2266 1 11900 1 0085 1531 0138 1 0197 2 0386 1 2615 2 12212 2 0086 901 0139 2 0198 3 0388 2 2617 9 15717 3 0087 944 0141 6 0199 35 0401 3 2671 7 16355 1 0088 517 0143 1 0200 1 0407 1 3069 3 17356 3 0089 449 0144 4 0201 84 0411 22 4241 1 18532 1 0090 543 0146 1 0203 1 0412 3 5101 15 23134 14 0091 354 0149 1 0204 1 0416 1 5157 9 \end{verbatim} \newpage \baselineskip = 14pt \LP \bf APPENDIX A\rm \eLP Structure definition to implement the Tierra virtual CPU. The complete source code for the Tierra Simulator can be obtained by contacting the author by email. \begin{verbatim} struct cpu { /* structure for registers of virtual cpu */ int ax; /* address register */ int bx; /* address register */ int cx; /* numerical register */ int dx; /* numerical register */ char fl; /* flag */ char sp; /* stack pointer */ int st[10]; /* stack */ int ip; /* instruction pointer */ } ; \end{verbatim} \LP \bf APPENDIX B\rm \eLP Abbreviated code for implementing the CPU cycle of the Tierra Simulator. \begin{verbatim} void main(void) { get_soup(); life(); write_soup(); } void life(void) /* doles out time slices and death */ { while(inst_exec_c < alive) /* control the length of the run */ { time_slice(this_slice); /* this_slice is current cell in queue */ incr_slice_queue(); /* increment this_slice to next cell in queue */ while(free_mem_current < free_mem_prop * soup_size) reaper(); /* if memory is full to threshold, reap some cells */ } } void time_slice(int ci) { Pcells ce; /* pointer to the array of cell structures */ char i; /* instruction from soup */ int di; /* decoded instruction */ int j, size_slice; ce = cells + ci; for(j = 0; j < size_slice; j++) { i = fetch(ce->c.ip); /* fetch instruction from soup, at address ip */ di = decode(i); /* decode the fetched instruction */ execute(di, ci); /* execute the decoded instruction */ increment_ip(di,ce); /* move instruction pointer to next instruction */ system_work(); /* opportunity to extract information */ } } void execute(int di, int ci) { switch(di) { case 0x00: nop_0(ci); break; /* no operation */ case 0x01: nop_1(ci); break; /* no operation */ case 0x02: or1(ci); break; /* flip low order bit of cx, cx ^= 1 */ case 0x03: shl(ci); break; /* shift left cx register, cx <<= 1 */ case 0x04: zero(ci); break; /* set cx register to zero, cx = 0 */ case 0x05: if_cz(ci); break; /* if cx==0 execute next instruction */ case 0x06: sub_ab(ci); break; /* subtract bx from ax, cx = ax - bx */ case 0x07: sub_ac(ci); break; /* subtract cx from ax, ax = ax - cx */ case 0x08: inc_a(ci); break; /* increment ax, ax = ax + 1 */ case 0x09: inc_b(ci); break; /* increment bx, bx = bx + 1 */ case 0x0a: dec_c(ci); break; /* decrement cx, cx = cx - 1 */ case 0x0b: inc_c(ci); break; /* increment cx, cx = cx + 1 */ case 0x0c: push_ax(ci); break; /* push ax on stack */ case 0x0d: push_bx(ci); break; /* push bx on stack */ case 0x0e: push_cx(ci); break; /* push cx on stack */ case 0x0f: push_dx(ci); break; /* push dx on stack */ case 0x10: pop_ax(ci); break; /* pop top of stack into ax */ case 0x11: pop_bx(ci); break; /* pop top of stack into bx */ case 0x12: pop_cx(ci); break; /* pop top of stack into cx */ case 0x13: pop_dx(ci); break; /* pop top of stack into dx */ case 0x14: jmp(ci); break; /* move ip to template */ case 0x15: jmpb(ci); break; /* move ip backward to template */ case 0x16: call(ci); break; /* call a procedure */ case 0x17: ret(ci); break; /* return from a procedure */ case 0x18: mov_cd(ci); break; /* move cx to dx, dx = cx */ case 0x19: mov_ab(ci); break; /* move ax to bx, bx = ax */ case 0x1a: mov_iab(ci); break; /* move instruction at address in bx to address in ax */ case 0x1b: adr(ci); break; /* address of nearest template to ax */ case 0x1c: adrb(ci); break; /* search backward for template */ case 0x1d: adrf(ci); break; /* search forward for template */ case 0x1e: mal(ci); break; /* allocate memory for daughter cell */ case 0x1f: divide(ci); break; /* cell division */ } inst_exec_c++; } \end{verbatim} \newpage \LP \bf APPENDIX C\rm \eLP Assembler source code for the ancestral creature. \begin{verbatim} genotype: 80 aaa origin: 1-1-1990 00:00:00:00 ancestor parent genotype: human 1st_daughter: flags: 0 inst: 839 mov_daught: 80 2nd_daughter: flags: 0 inst: 813 mov_daught: 80 nop_1 ; 01 0 beginning template nop_1 ; 01 1 beginning template nop_1 ; 01 2 beginning template nop_1 ; 01 3 beginning template zero ; 04 4 put zero in cx or1 ; 02 5 put 1 in first bit of cx shl ; 03 6 shift left cx shl ; 03 7 shift left cx, now cx = 4 ; ax = bx = ; cx = template size dx = mov_cd ; 18 8 move template size to dx ; ax = bx = ; cx = template size dx = template size adrb ; 1c 9 get (backward) address of beginning template nop_0 ; 00 10 compliment to beginning template nop_0 ; 00 11 compliment to beginning template nop_0 ; 00 12 compliment to beginning template nop_0 ; 00 13 compliment to beginning template ; ax = start of mother + 4 bx = ; cx = template size dx = template size sub_ac ; 07 14 subtract cx from ax ; ax = start of mother bx = ; cx = template size dx = template size mov_ab ; 19 15 move start address to bx ; ax = start of mother bx = start of mother ; cx = template size dx = template size adrf ; 1d 16 get (forward) address of end template nop_0 ; 00 17 compliment to end template nop_0 ; 00 18 compliment to end template nop_0 ; 00 19 compliment to end template nop_1 ; 01 20 compliment to end template ; ax = end of mother bx = start of mother ; cx = template size dx = template size inc_a ; 08 21 to include dummy statement to separate creatures sub_ab ; 06 22 subtract start address from end address to get size ; ax = end of mother bx = start of mother ; cx = size of mother dx = template size nop_1 ; 01 23 reproduction loop template nop_1 ; 01 24 reproduction loop template nop_0 ; 00 25 reproduction loop template nop_1 ; 01 26 reproduction loop template mal ; 1e 27 allocate memory for daughter cell, address to ax ; ax = start of daughter bx = start of mother ; cx = size of mother dx = template size call ; 16 28 call template below (copy procedure) nop_0 ; 00 29 copy procedure compliment nop_0 ; 00 30 copy procedure compliment nop_1 ; 01 31 copy procedure compliment nop_1 ; 01 32 copy procedure compliment divide ; 1f 33 create independent daughter cell jmp ; 14 34 jump to template below (reproduction loop, above) nop_0 ; 00 35 reproduction loop compliment nop_0 ; 00 36 reproduction loop compliment nop_1 ; 01 37 reproduction loop compliment nop_0 ; 00 38 reproduction loop compliment if_cz ; 05 39 this is a dummy instruction to separate templates ; begin copy procedure nop_1 ; 01 40 copy procedure template nop_1 ; 01 41 copy procedure template nop_0 ; 00 42 copy procedure template nop_0 ; 00 43 copy procedure template push_ax ; 0c 44 push ax onto stack push_bx ; 0d 45 push bx onto stack push_cx ; 0e 46 push cx onto stack nop_1 ; 01 47 copy loop template 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] 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 inc_b ; 09 60 increment bx 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 if_cz ; 05 66 this is a dummy instruction, to separate templates nop_1 ; 01 67 copy procedure exit template nop_0 ; 00 68 copy procedure exit template nop_1 ; 01 69 copy procedure exit template nop_1 ; 01 70 copy procedure exit template pop_cx ; 12 71 pop cx off stack pop_bx ; 11 72 pop bx off stack pop_ax ; 10 73 pop ax off stack ret ; 17 74 return from copy procedure nop_1 ; 01 75 end template nop_1 ; 01 76 end template nop_1 ; 01 77 end template nop_0 ; 00 78 end template if_cz ; 05 79 dummy statement to separate creatures \end{verbatim} \newpage \LP \bf APPENDIX D\rm \eLP Assembler source code for the smallest self-replicating creature. \begin{verbatim} genotype: 0022abn parent genotype: 0022aak 1st_daughter: flags: 1 inst: 146 mov_daught: 22 breed_true: 1 2nd_daughter: flags: 0 inst: 142 mov_daught: 22 breed_true: 1 InstExecC: 437 InstExec: 625954 origin: 662865379 Wed Jan 2 20:16:19 1991 MaxPropPop: 0.1231 MaxPropInst: 0.0568 nop_0 ; 00 0 adrb ; 1c 1 find beginning nop_1 ; 01 2 divide ; 1f 3 fails the first time it is executed sub_ac ; 07 4 mov_ab ; 19 5 adrf ; 1d 6 find end nop_0 ; 00 7 inc_a ; 08 8 to include final dummy statement sub_ab ; 06 9 calculate size mal ; 1e 10 push_bx ; 0d 11 save beginning address on stack in order to `return' there nop_0 ; 00 12 top of copy loop mov_iab ; 1a 13 dec_c ; 0a 14 if_cz ; 05 15 ret ; 17 16 jump to beginning, address saved on stack inc_a ; 08 17 inc_b ; 09 18 jmpb ; 15 19 bottom of copy loop (6 instructions executed per loop) nop_1 ; 01 20 mov_iab ; 1a 21 dummy statement to terminate template \end{verbatim} \newpage \LP \bf APPENDIX E\rm \eLP 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. \newpage \begin{verbatim} 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) \end{verbatim} \end{document}