The Biological Validity of Evolving Artificial Life

The Biological Validity of Evolving Artificial Life A.C. Dam Faculty of Electrical Engineering, Mathematics and Computer Science University of Twente a.c.dam@student.utwente.nl ABSTRACT This paper describes a research on the biological validity of virtual ecosystems. Artificial Life systems are designed in the pursuit of creating an instance of life in a computational environment. The reproduction mechanisms of these systems are compared with nature s reproduction mechanisms as described by Ernst Mayr. This research determines if the ecosystems give a correct representation of natural evolution, and for what goal evolutionary scientists can use these systems for biological science. Keywords Evolution, Artificial Life, Ecosystem, Biological Science. 1. INTRODUCTION Darwin s famous book On the origin of species caused a revolution in evolutional science in 1859. Ever since, researchers are revealing more and more information about the mechanisms that drive evolution. In the last century major discoveries like the genome, DNA and meiosis revealed some of the secrets about evolution. Though a lot is known, the only way for scientists to observe evolution in practice, is to analyse mutating bacteria. Evolution of sexual reproducing species is a slow process, observing evolving species would take thousands of years. Until recently, researchers interested in studying this form of evolution were bounded by fossil discoveries. Throughout the last fifteen years, researchers are studying artificial life within virtual ecosystems [Mar00]. These systems are created to simulate nature and reach unimplemented complexity using nature s mechanisms of evolution. A virtual ecosystem is a world filled with digital entities, which interact with their environment and each other. The survival of these entities depends on their fitness within the virtual world. Surviving entities are able to reproduce sexually. Each entity has a genotype, which determines its phenotype. The genotype usually is some form of bit-string, the phenotype is a compilation of this bit-string determining the entity s physical characteristics and behaviour. Surviving entities are able to combine their genotype with that of another surviving entity, leaving offspring with an appearance and behaviour inherited from both its parents. The starting conditions of such systems are a few entities with simple genotypes. When a system starts its simulation, the entities start eating, competing and reproducing. Slowly the entities will evolve into species and these new species will adapt to their environment over generations. The simulation gives an Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission. 3rd Twente Student Conference on IT, Enschede June, 2005 Copyright 2005, A.C. Dam, University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science actual view on the evolution of sexual reproducing entities, showing speciation, adaptation, convergence [CD98], creative solutions, unique behaviour [LCY04] and complexity [CNP98]. Though virtual ecosystems have enough potential to be an interesting object for studying evolution [DP97], the fact remains that these systems are designed and implemented by computer scientists trying to create artificial life while borrowing nature s mechanisms instead of biological scientists trying to simulate evolution of life as we know it [Mil95]. Therefore the question remains whether these systems can actually be seen and used as an artificial representation of nature. This research offers an answer to the question on this biological validity of the current artificial life systems. Three ecosystems were analyzed and their implemented reproduction mechanisms were compared with nature s reproduction mechanisms. Section 2 describes the methodology of the performed research. Because there are too many ecosystems available and this research is bounded by a time constraint, three ecosystems are selected for further analysis, this will be done in section 3. Section 4 contains a detailed analysis of the three selected virtual ecosystems, together with the selection criteria. The results of this detailed analysis and the comparison of the three systems on the basis of these acquired results can be found in section 5. This paper concludes in section 6 with an assessment of the usability of the current ecosystems for biological and evolutionary research, together with recommendations for further work. 2. METHODOLOGY At the beginning of this research, the following operational questions were drafted. These research questions served as a guideline throughout this research. They led the way to an answer on the question of the scientific validity of the current virtual ecosystems. Is the specification of the reproduction and selection mechanisms in the current virtual ecosystems correct? And if not, what are the differences? Which of the current virtual ecosystems gives the most correct representation of evolution in nature? For which goals could current virtual ecosystems be used as scientific material to examine the process of evolution? There are many ecosystems available for this research and because a detailed analysis is very time consuming, a selection has to be made about which systems will be considered for further analysis. The candidate ecosystems are PolyWorld [Yea94], TooDee [Cro02], Gaia [GPL97], Creatures [CG99], Framsticks [Kom00] [KU99] and Tierra [Ray91]. Three of these six candidate systems were selected for further study. The selection criteria, as well as the selection procedure itself are outlined in the following section together with a short overview of every system. The reproduction and selection mechanisms of the selected systems were compared with the mechanisms used by nature as described in the book What evolution is [May01] written by

the late Ernst Mayr, who is considered an expert on the field of evolutionary science within the scientific biological field of research. This comparison functions as the answer on the question about the correctness and differences of the system specifications with respect to nature. The research criteria, as well as the comparison with nature s mechanisms of the selected ecosystems, are described in section 4. Based on this analysis of correctness and differences, the determination of which ecosystem gives the most correct representation of nature was made. This procedure is described in section 5. First, the results of the analysis of section 4 will be outlined, followed by a comparison of the ecosystems on the basis of these results. Not only the current best representation will be chosen, but also the system which has the greatest potential to give a correct representation of nature s mechanisms in the future. Based on the previous research done on these systems, a conclusion was made about how biologists could use these systems to observe evolution in practice. This includes for which goals the different systems could be used. 3. PRESELECTION OF SYSTEMS In this section the systems that are to be analyzed further in this research will be selected. This selection will be based on the following criteria: Background of the system designers Goal / purpose of the system Comprehensiveness of system documentation System potential The background of the system designers gives a clue on the amount of research done to create the systems, it also tells from what perspective the designers operate. The purpose of the system is important to create insight on the amount of effort the designers took the make a correct system with regard to nature. The comprehensiveness of documentation is indispensable to give a good analysis of the system and therefore very important. The potential of the system is one based on first impression, it is taken into consideration because the goal of this research is to improve overall biological quality of virtual ecosystems. It is desirable to select systems that show great diversity to each other, this gives a change to look at digital ecosystems from more than one perspective. The systems that will be considered in this section are PolyWorld, Gaia, TooDee, Creatures, Framsticks and Tierra. 3.1 Global analysis of the ecosystems 3.1.1 PolyWorld PolyWorld [Yea94] is designed by one person, Larry Yeager who was an employee of the Apple Computer Inc. while creating this system. PolyWorld was created as an extra source of knowledge for evolutional research, the overall goal was to enhance the understanding of living systems. More specifically, the PolyWorld system intended to bring the fundamental components of real living systems into an artificial system. The study on evolvement of behavioural ecology and neurophysiology are also among the system goals. The system is very well documented. The system is a two-dimensional world with entities moving around, these entities have a genome and can reproduce themselves. The genome of an entity specifies its behaviour, the genome is produced as a combination of the genomes of mating entities. Much of the evolutionary mechanisms are implemented in this system and the entities are free to evolve in any direction. The two dimensional world serves as a good output to study the simulation. The fitness of entities is based on their capacity to survive. This system shows great potential and some of the other examined systems are inspired by PolyWorld. 3.1.2 TooDee TooDee [Cro02] is just like PolyWorld created by one person, Mark Crossfield, a graduate student at the Computer Science Department of Nottingham University. The systems goal is to create a two-dimensional world filled with entities which show realistic behavioural patterns. The system is inspired by PolyWorld and is well documented. Because this system seems to be a simplified version of PolyWorld and lacks a certain amount of complexity (the system is restricted to a predator-pray relationship), the potential of the system seems quite low for this research. This is also caused by the designers goal to create a system is which behavioural patterns can be analyzed instead of evolution itself. 3.1.3 Gaia Gaia [GPL97] is created as an ecology simulator in order to study learning, evolution and population dynamics. The system is designed by a team of researchers of the Systems and Robotics Institute in Portugal led by Nuno Gracias. The goal of the system was to test the current knowledge about evolution and learning. The designers intended to use the system to analyse whether the known biological mechanisms can explain the evolution of organisms with simple nervous systems. The system was based on PolyWorld and tried to reproduce its results, extended with extra features to analyse population dynamics. The system is well documented, but not as detailed as PolyWorld. Because this system is actually a reproduction of PolyWorld extended with some features, it shows about the same potential. The extensions however do not contribute to the system quality. The system is more directed than PolyWorld in order to study population dynamics. 3.1.4 Creatures Creatures [CG99] is an entertainment-software product developed by Mindscape in cooperation with Cyberlife Technology Ltd. The goal of the system is to entertain its users. Although the system is not designed for evolution studies it does implements some of the biological evolutionary mechanisms and might be interesting to analyse. The developer has sold over 500.000 copies and each copy contains about ten individuals, the individual groups are connected online, and thus creating a quite large ecosystem. The system is analysed and documented by Dave Cliff of the Artificial Intelligence Laboratory at Massachusetts. Short analysis shows however that there is only one possible species in this virtual world, which contains only a maximum of ten individuals in a local niche, and is not subjected to severe selection pressure. Because evolution is based on natural selection, speciation and can only evolve as a population over many generations [May01], a system with only one species, with very small populations and hardly any selection, has poor potential to be a correct representation of nature. 3.1.5 Framsticks Framsticks [KU99] [Kom00] is designed by a research team of the Institute of Computing Science of the Poznan University in Poland, led by M. Komosinski. The goal of this team is to build a virtual world that is more complex and attractive to the observer than other virtual ecosystems. Therefore the designers have created a 3d-system in which entities can evolve. Entities have a body based on their genome, and physics is a vital element of the system. The mechanisms used in this system are well documented.

Because this system allows the user to analyse the body development of a population, it has great potential as a source for evolutional studies. Although the path of evolution is bounded (entities are selected on their speed of locomotion), the way to reach this goal is open-ended. 3.1.6 Tierra Tierra [Ray91] was created as an experiment by a team of researchers at the Santa Fe Institute of Complexity led by Thomas Ray. The researchers built the system with the desire to observe a different instance of life, an evolutionary process that is not based on carbon. Because currently, only one instance of life is known, the knowledge and understanding of evolution is limited. Tierra was created to broaden the knowledge of life and the evolutionary process. The background of the researchers that have worked on Tierra reach from computer scientists to tropical ecologists. The system is documented in great detail. The evolving entities in the system are actually self-replicating computer processes. This is quite unique in comparison with the other systems and therefore very interesting. The designers did not try to replicate nature itself, but just used its evolving mechanisms to create another instance of evolution. The evolution of the entities is unbounded and undirected. This angle of approach shows great potential because the only restrictions are that of the environment, the computer running the processes. Entities are free to exploit every niche of the system just like entities in nature are free to exploit every niche of earth. 3.2 Selection of three ecosystems The analyses of the systems above are very global, in the next section detailed analyses of the systems will be given. Because of time constraint on this research a selection of ecosystems to be analyzed in great detail has to be made. After the global analysis it is clear that PolyWorld, Gaia and TooDee show great parallels to each other. Because it is interesting to analyse systems with different perspectives to evolution, only one of them will be selected for further analysis. Of these three systems PolyWorld is selected in favour of Gaia because Gaia does not seem to add important features with an evolutional aspect, and also because PolyWorld is better documented and for that reason more useful as a study object. TooDee seems just a simplified version of PolyWorld, the quality of documentation, the complexity of the system and the evolutional knowledge of the designer is inferior to PolyWorld its designer and for that reason PolyWorld is selected for further analysis. Of the three remaining systems, Creatures shows the lowest potential. This system lacks the population properties defined by Mayr. Since Tierra is very interesting because of the total freedom of development of the entities, this system is selected. Framsticks is also selected for the high complexity of the system and its unique 3d feature which allows a good analysis of the systems results to evolutional researchers. 4. SYSTEM ANALYSIS In this section the ecosystems PolyWorld, Framsticks and Tierra will be examined. The subjects of examination are composed of a summary of elements of evolution outlined in What evolution is by E. Mayr. These elements form the basic of our knowledge on evolution and are widely excepted throughout the field of evolutional studies. It should be noted that this analysis determines if the ecosystems offer a correct implementation. An ecosystem does not necessarily have to satisfy all of the elements to be useful for biological scientists. Although it does have to do this to be called correct. The subjects of examination are the following: Implementation of the genotype (building plan) and phenotype (appearance and behaviour): the phenotype, the product of the genotype should not be able to in any way influence the genotype. Only through reproduction new genotypes can be produced (figure 1 shows a visual representation of the genotype inheritance). The phenotype s fitness in their environment determines whether it is able to reproduce. In theory two entities which have the same phenotype could have completely different genotypes (convergence). (Mayr pp 89..93). Reproduction: Reproducing entities must deliver a new genotype which is a variation of their own genotype (inheritance). The new genotype determines what the characteristics will be of the new entity. Variation can be reached through mutation, and through the mechanism of cross-over, which means that the new genotype is a combination of its parents. (Mayr pp 88..100, 103..113). Note that natures mechanism of cross-over is very complex, in this research it is assumed that natures mechanism does not have to be exactly copied, more important is that the mechanism used has the same possible outcomes. Fitness and natural selection: Entities in an environment are subjected to natural selection (figure 1 shows a visual representation on the subjection of phenotypes to selection pressure), some are able to reproduce, others are not. The selection pressure is provided by both the environment (the restricted amount of energy) as well as the competition between entities, competition on food as well as sexual competition. Entities that cope best with their environment will survive and reproduce, they are said to have the highest fitness. This process is the core mechanism of evolution, enabling fit entities to reproduce those characteristics that make them fit entities. Though the variation might be random, the selection is not, this way a population can, over many generations, evolve and adapt to its environment. (Mayr pp 117..121) Speciation: Every entity belongs to a species. In this research the definition of a species is as follows: Two entities belong to the same species if they have a common ancestor and if they can mate and reproduce fertile offspring. New species can only originate when a population gets isolated, this can be geographically, but also sexually. (Mayr pp 174..176) Population: Evolution does not occur on individuals. Every species is composed of a number of local variable populations. Within a population every individual is different from every other individual. These differences cause different probability of survival or reproduction and are (in part) heritable. The local populations (and not the individuals) are said to evolve and adapt through natural selection. This is called population thinking and is considered to be the foundation of modern evolutionary theory. (Mayr pp 75..77,148) Directedness: In principle evolution is undirected, environmental changes cause populations to either adapt (evolve) or extinct. Even though steering is possible when one controls the environmental changes and thus defines a different natural selection. This could be called breeding. (Mayr pp 167)

Figure 1. The phenotype (P), and not the genotype (G), is subjected to selection pressure. The genotype is passed on to the next generation (G1/2), resulting in an new phenotype (P1/2). 4.1 PolyWorld PolyWorld [Yea94] is a two-dimensional world filled with entities called organisms. Each organism possesses a genome, which determines its appearance (size, strength and colour) and behaviour (through neurons which link vision input to motor output). Organisms need food to survive, which forms the organisms initial quest in the virtual world. Food can be found in the form of food cells, a dead organism will also turn into a food cell. In order to reproduce in PolyWorld, organisms need to find a mate. Organisms do not have a predefined gender, so in theory every other organism is a potential mate. A population of organisms can evolve by means of appearance (becoming big and fast) and by means of behaviour (food searching strategy). 4.1.1 Implementation genotype-phenotype Every organism (the phenotype of a genotype) in PolyWorld has a unique genome. The genome defines the neural architecture of the organism and thus its behaviour. The organisms speed, size, strength, colour, life span is also hard coded in its genome. It is not possible for an organism to alter its genome based on experiences during its lifetime. Although two organism could in theory have the same behaviour with different genomes, this cannot be said about the appearance of an organism. This is implemented one-on-one. When comparing to the list of elements drafted above, the implementation of the genotype-phenotype relation is almost correct. Since PolyWorld is a simulation that is more interested in the evolution of behaviour instead of appearance, this flaw could be overlooked. 4.1.2 Reproduction Organisms in PolyWorld need to reach a certain age to be able to reproduce. When two organisms reproduce, a new genome is created which is a cross-over of its parents genomes (inheritance). Slight mutation of genes within the new genome is possible. This implementation adds a source variation in PolyWorld and thus makes evolution possible. It is however not possible to add new genes or remove useless genes in any way, which is a constraint to complexity. This complexity constraint does not make the model incorrect however. 4.1.3 Fitness and natural selection Organisms in PolyWorld have to compete with each other for food. This can be on the speed front, reaching food cells before others, it can also be through a predator-pray relationship (cannibalism). Organisms being killed by others, dying from starvation or not successful in finding a mate to reproduce, are considered unfit. Organisms who manage to find food and successfully reproduce are considered fit. The constant need for food and the risk of being killed by another organism provides the natural selection. Sexual selection is not spotted in the system, but it is possible in theory. Consider the occurrence of a species only interested in mating with green mates, a red organism should be considered unfit. PolyWorld has implemented a correct representation of fitness and natural selection. 4.1.4 Speciation Yeager defines the term species in PolyWorld as: Groups of organisms carrying out a common individual behaviour that results in distinctive group behaviours. This definition is in conflict with the Darwinian definition according to Mayr. Groups of organisms showing similar behaviour are considered convergent, but not related and thus do not necessarily belong to the same species. In PolyWorld every organism can mate with any other organism and produce fertile offspring. Though the emergence of groups showing different behaviour to other groups when geographically isolated does occur, this can not be called speciation. It is similar to the geographical isolation of human in example Africa and Asia, although the appearance (phenotype) is different, these humans belong to the same species. When combining the two groups again in PolyWorld, instead of a coevolution of species, a merge of characteristics will occur, or an extinction of the group with inferior behaviour. This is caused by the fact that organisms have a fixed genome, so cross-over is always possible. Yeager has tried to solve this problem with an optional miscegenation function. This function influences the likelihood of dissimilar organisms producing viable offspring. This function, originally implemented to explore more genetic recombinations, only solves the problem partially. To conclude the above, the implementation of speciation in PolyWorld is partially incorrect. 4.1.5 Population As stated before, PolyWorld only has one single species. This species in fact is able to consist of several populations, when isolated. An isolated population consists of many individuals who are genetically different. These differences, like size and vision perception behaviour, define their fitness. A population in PolyWorld adapts and evolves as a whole, single individuals do not evolve, they just reproduce when they prove to be fit enough to survive and find a mate. PolyWorld has successfully implemented a correct representation of population thinking. 4.1.6 Directedness Life on earth is considered an undirected process (creationists critics left out of consideration) which has originated from one

source. This is not entirely true for PolyWorld. During a simulation run, the success of a population is continuously evaluated. Populations which are able to replenish their population number through births are said to be successful, and demonstrate a Successful Behaviour Strategy (SBS). Until in a simulation SBS has emerged, the run is evaluated with an online Genetic Algorithm (GA). This GA guarantees that a minimum number of organisms inhabit PolyWorld. When this number drops below the minimum, new organisms are inserted in the simulation run (at random or as offspring of the fittest organisms). Although the use of a GA guarantees that a population will not suffer extinction, it is not a correct representation of nature. A population is guided to success, although their genome is not capable of doing so on its own strength. 4.2 Framsticks The world of Framsticks [KU99] [Kom00] is a 3-dimensional world with entities called creatures. This virtual world consists of land and water and has gravitational forces similar to the ones on earth. The creatures are made of sticks and have brains constructed of neurons. Neurons are attached to muscles, receptors and each other. This setting allows creatures to base their movement on their environment. The appearance and behaviour of a creature is based on their genes. To survive in the Framsticks world, a creature needs to gain energy. In order to do this, a creature can eat food cells or feed on each other. After a creature dies, a fitness function determines if the creature may reproduce. This function is defined by the user and can be based on properties of the creature, like speed and lifespan. The lifespan of a creature is based on its amount of energy, which is an internal variable. 4.2.1 Implementation genotype-phenotype The behaviour and appearance of a creature in Framsticks depends completely on its genotype. Genotypes can only be altered during the process of reproduction. There are three possible implementations of the genotype representation, the user can select which representation will be used in a specific run. The first representation is basic direct encoding, this form of genotype is a list of all the parts of the organism and how they are attached to one another. It is similar to a building plan of an architectural structure, describing how the bolts should be attached to the girders. The second representation is recurrent direct encoding, this involves a genotype made of recurrent expressions which leads to a tree-like structure of the morphology. The third representation is called developmental encoding, this genotype form describes how the creature should be build. The creature starts with an individual cell, and the genotype describes if this cell should divide, grow or change other parameters. This way the creature develops as described in its genes. This representation also allows that two completely different genotypes construct the same phenotype (convergence). The third genotype representation is very interesting since it is similar to the basic functioning of DNA. In terms of correctness, the third representation seems more correct then one-on-one encoding and should be preferred by evolutionary biologists studying evolution. 4.2.2 Reproduction Framsticks has a central gene pole, which is basically a list of all the genotypes. Each genotype entry represents a group of identical genotypes and has an evaluation value. During a run, a genotype is picked out of the gene pole, this genotype is subjected to reproduction mechanisms like cross-over and mutation. After that, the phenotype is constructed and placed in the virtual world (and becomes a creature). The creatures in Framsticks are evaluated during their life, the subjects of evaluation are decided by the user. This evaluation corresponds directly to its reproduction abilities. When a creature dies, its genotype is reinserted into the gene pole, together with its evaluation value. The number of creatures in Framsticks is user defined, when a creature dies, a new genotype is selected from the gene pole. This selection is randomly, though genotypes with higher evaluation values have a bigger chance of being selected. This mechanism of reproduction differs from natures mechanism, where all genotypes are present in the world, when this is not the case, a species is called to be extinct. Framsticks works differently, only a selection of all genotypes is present in the virtual world, which makes the interaction of different species a matter of chance and from a statistical point of view, a genotype will never extinct. Also the fact that a creature does not reproduce during its life span, but afterwards, is in conflict with nature s mechanism. This method of reproduction used in Framsticks is not correct. 4.2.3 Fitness and natural selection As described above, the fitness of a creature is determined externally, by user defined definitions. Commonly used fitness variables are maximum speed, average speed, life span and distance from starting point. According to the fitness of a genotype, the chance of reproduction is altered. This is the natural selection element in Framsticks, assuring that fit genotypes have better chances on reproduction than less fit ones. Although this method seems to have the same outcome as natural selection, it is fundamentally different. In nature, individuals are subjected to selection pressure, their fitness allows them to survive and reproduce. In Framsticks entire species are subjected to a form of selection pressure, the fitness of an individual member determines the reproduction abilities of the entire species. The representation of natural selection and fitness is incorrect in comparison with natures mechanism. 4.2.4 Speciation Because there is no actual mating involved in Framsticks, the definition of a species as stated before is not applicable. In Framsticks a species is called a group of similar individuals that share the same ecological niche. When a species is highly similar to other co-existing species, its fitness value is lowered. During the procedure of cross-over, if possible, some parts of the genotype of similar species are exchanged, which should also allow speciation. The implementation of developmental genotypes should allow speciation as defined by Mayr. When two genotype are too dissimilar to each other, they will not be able to merge and thus, do not belong to the same species. Though, because the reproduction mechanism is not implemented this way, this potential to correct speciation is lost. The definition of species used in Framsticks is wrong, which leads to a wrong implementation of speciation. Although during a cross-over procedure, elements can be exchanged between single creatures, this cannot be called mating, which is essential to speciation. 4.2.5 Population As stated above, only a fraction of the total amount of genotype is present in the virtual world. This causes a situation where individuals of a population are not able to compete with each other. This contributes to a distorted image of a population. Adaptation is reached, not through the method of population thinking, but through the method of adapting individuals. This is because a successful creature is offered a second change in

the virtual world, but only with a slight (random) alteration in its genotype. This implementation of populations is incorrect in comparison with Darwinian evolution. 4.2.6 Directedness The fitness function in Framsticks is user defined, this causes a directed evolution. When the user defines that high speed creatures have greater fitness, then high speed creatures will emerge in the virtual world. This form of fitness establishment makes open-ended evolution impossible and could better be called breeding than evolution. The guided evolutionary processes are actually deliberately implemented by the system designers to boost the system s efficiency. Although the implementation is incorrect from an evolutionary point of view, is does offer a unique way to observe the development of body structure and the creation of limbs through an evolutionary process, where the best solution also depends on the previous body structure instead of only the environment. 4.3 Tierra Tierra [Ray91] is an ecosystem fundamentally different to the other systems analyzed above. It does not consist of creatures moving through a two-dimensional world. In this virtual system, processes live in a virtual computer and compete for CPU cycles and memory space (RAM). These processes are built of assembler code and reproduce by means of copying their own program code in a new memory block. This assembler code consists of 32 five-bit instructions. After reproduction, the memory block containing a copy of its ancestor, is a new process with its own program counter (executing parallel to its ancestor). The Tierran operating system consist of a slicer and a reaper. The slicer divides slices of CPU time between the processes, which are placed in the slicer queue. When the slice size is relatively small compared to the generation time of the processes, the processes approximate parallel execution. The reaper is a mechanism that eliminates processes when they fill up Tierra s memory. New processes enter the bottom of the reaper queue. When Tierra s memory reaches a specified level of congestion, the process on top of the reapers queue stops execution. Program errors within the processes will make that process move up the reapers queue, execution of difficult instructions will make processes move towards the bottom of the queue. This mechanism ensures that fit processes will live longer. An external process provides mutation by means of flipping bits in executing processes and in reproduction procedures. 4.3.1 Implementation genotype-phenotype The assembler code of a process in Tierra could be seen as its genotype. The behaviour of the process would then be its phenotype. Since the behaviour of a process is dependent on its genotype and the genotype is mutated only by reproduction, the implementation seems correct. Although it is possible to flip bits (and thus changing the genotype) of a process during execution, it does serve as a response to the environment. Therefore it does not violate the hard genotype property. Note that two processes with different implementations (genotypes) could have the same function. 4.3.2 Reproduction Processes in Tierra are mainly reproducing entities. Their reproduction mechanism is a part of code which enables them to copy their code (genotype) into a new space of memory. After copying, this new copy is divided from its parent and functions as a separate process. External mechanisms provide random mutations in the copied code. This is basically the same mechanism as exploited by bacteria in nature. Though crossover (and thus mating) is not used, it is a correct representation because of the inheritance properties. The absence of cross-over however does influence the correctness of the speciation representation as will be seen later. 4.3.3 Fitness and natural selection Tierra does not have an external fitness function, the behaviour of processes compared to each other in the virtual world determines whether processes can continue to execute (they will not end up on top of the reaper queue, if they produce less error codes etc.). This mechanism provides the natural selection and fitness definition. Fit processes are able to execute longer, and thus gain more memory and CPU slices, with the result that they will be able to reproduce offspring. Processes with redundant code evidently suffer extinction because it takes more CPU slices to reproduce. This representation provides a similar form of natural selection in nature and is therefore a correct implementation. 4.3.4 Speciation As ascertained before, processes in Tierra do not mate, there is no cross-over between genotypes of different processes implemented. Ray poses that if this were the case (which could be implemented as an extension), the size of the genotype (number of code lines) would determine to which species a process belongs. Two processes with different size would have difficulties to reproduce. Processes with the same amount of code, would not in fact be the same but could be able to reproduce a new process. But in this theory, processes with different ancestors that do share the same size would belong to the same species, which is incorrect according the definition given at the beginning of section 4. Through reproduction a drastic change could occur in the genome size, this newly created process could be able to reproduce with another process of the same size (but of different origin). With other words, a child of a process could suddenly belong to a different species than its parent. Speciation is not correctly implemented, and, because of the previous example, the proposed extension given by Ray would also be incorrect. 4.3.5 Population The implementation of Tierra does not involve the population thinking model as stated by Darwin. Processes do not belong to a population, but are independent of each other in adaptation to the environment. Evolution in Tierra does in fact occur on individuals and adaptation is reached through the offspring (copies) of successful individuals. This could be compared with the adaptation of bacteria, but is incorrect from the perspective of this research. 4.3.6 Directedness Because there is no user defined fitness function and the processes are free to evolve in every way possible to take advantage of their environment, the path of evolution is openended. In theory, every outcome is possible and there is no optimized form to which the designer pleases the processes to evolve. Consequently evolution in Tierra is undirected. 5. COMPARISON In this section the three ecosystems that were analyzed are compared with each other. This section ends with a selection of the ecosystem which has implemented the most correct representation of nature s reproduction mechanisms and for that reason has the best approximation of a simulation of natural evolution.

5.1 Results of analyses The results of the system analyses are outlined in Table 1, every system has got a grade reaching from - - to + + on all analyzed subjects. Some of the grades are self-evident given the research before, some will need justification, which will be given below. Table 1. System qualities and shortcomings PolyWorld Framsticks Tierra Genotype + + + + Reproduction + - + Fitness + + - + Speciation - +/- - Population + - - Directedness +/- +/- + + Framsticks has a + + on the genotype-phenotype representation because of the developmental encoding. Though the implementation of the other systems is also correct, that of Framsticks is better because of the parallels with DNA. PolyWorld has a + + on the fitness and natural selection subject, because its implementation is more correct than that of Tierra because of Tierra s external reaper process. Tierra s implementation is not incorrect, therefore Tierra has a + and PolyWorld a + +. Although none of the systems has a correct implementation of speciation, Framsticks does have a +/-, this is because the genotype-phenotype implementation shows great potential for a correct implementation of speciation. With some adjustments Framsticks should be able to implement a correct model of speciation. Tierra has a + + on directedness for the reason that the system seems to have no goal at all other than the creation of artificial life. The shape of this created life is irrelevant. The other two systems, have bounded (but not fully constraint) the direction of evolution. 5.2 Selection best representation All systems miss fundamental correctness on some parts in their evolutionary mechanisms. Besides that, the subjects of evaluation do not have the same weight with respect to each other, some properties seem more important than others. Also the potential of the system to change and give a correct model in future adaptations or extensions is important. When looking at the current systems evaluated in this research, PolyWorld succeeded in giving the most correct representation of nature s evolutionary model and therefore could be called the winner. Though it has to be noted that Tierra and Framsticks also have great potential. PolyWorld is quite fixed and has less potential to give a correct representation in the future because of the fixed size genome. Framsticks with its developmental genome and complex organism structures has perhaps more prospective in future work. 6. CONCLUSION In conclusion of this research it is important to evaluate if the current systems can be used by evolutionary biologists and for what goal. The previous sections showed that none of the systems analyzed where able to give a completely correct implementation of natural evolution. For this reason some recommendations are made of how to improve the biological quality of current systems, or how to create a new and correct system using the best elements of the systems evaluated in this research. It has to be noted that a full simulation of nature is, at this stage, too complex. The recommendations only tend to bring evolutionary studies one step further. 6.1 Current usability for biological scientists Though none of the ecosystems give a totally correct implementation of nature s evolutionary mechanisms, this does not mean that this fact makes them useless. All the systems have good qualities, with which some aspects of evolution can be observed. The only subject of which none of the ecosystems have succeeded, is giving a correct implementation of speciation. Because speciation is a vital element of macro-evolution, the current ecosystems are not capable of providing macroevolution observations to evolutionary researchers. PolyWorld could be used by researchers to observe the evolution of isolated populations and there adaptive response to a new environment. Because speciation is not part of PolyWorld, only short-term evolution can be observed. But short-term in this context is only relative, adding up to thousands of years in evolution in comparison with nature. The distribution and adaptive behaviour development of the human species, in example, took many thousand of years. A simulated and simplified version could be observed in PolyWorld by a run taking only a few hours. Framsticks, at the current stage of development, can be used to observe the adaptation of organism to its environment by means of body development. The emerge of complex limbs can only be reached through step by step evolution in nature, every step has to be useful. Framsticks gives a good opportunity to observe this form of evolution in practice instead of fossil analyses (where the fossil record is far from complete). Tierra has succeeded in giving an undirected model of bacterial evolution. This could be a great mechanism for studying the evolvement and adaptation of bacteria. The advantage of Tierra would in this case be the time factor. But as every evolutionary biologist will agree, this form of simulation is not a valuable gain for evolutionary research. Bacteria in nature divide very quickly and show adaptation on short timescales [PSM99]. Observing a simulation of nature seems to be useless from this perspective when the real thing is also available for observation. For this reason Tierra, in its current form, is just a project for the pursuit of creating synthesized life instead of an instrument to observe natures evolution in a simulation. 6.2 Recommendations The current versions of the analyzed ecosystems all seem to lack a fundamental amount of correctness and biological validity on some parts. The current ecosystems do not provide a platform to observe macro-evolution, a phenomenon that is too slow to observe in nature besides analyses of the fossil record, which is very incomplete. An ecosystem with a correctly implemented model of macro-evolution would offer a great advantage to evolutionary biologists. Speciation is the key to macro-evolution and the evolvement of co-existing species. Combination elements of the Framsticks system together with elements of the PolyWorld system could lead to a correct implementation of speciation. When the mechanism of developmental genotype encoding and complex organism structure of Framsticks is combined with the mating procedure in PolyWorld, this may lead to speciation in geographically separated populations. When recombining these populations after some time, the difference in genotype will offer a constraint on mating and producing viable or fertile offspring. Speciation would be a fact, and the system could show a coevolution of different species, different populations of the same species and an actual evolving world.

REFERENCES [CNP98] Calabretta, R., Nolfi, S., Parisi, D., and Wagner, P. - A case study of the evolution of modularity: towards a bridge between evolutionary biology, artificial life, neuro- and cognitive science - Proceedings of the Sixth International Conference on Artificial Life: pp. 275-284, 1998 - MA: MIT Press, Cambridge [CD98] Channon, A.D., and Damper, R.I. - Perpetuating evolutionary emergence - From Animals to Animats 5, 1998 - Wilson, MIT Press, Cambridge [CG99] Cliff, D., and Grand, S. - The Creatures Global Digital Ecosystem - Artificial Life 5: pp. 77-94, 1999 MIT press, Cambridge [Cro02] Crossfield, M. TooDoo, An Evolutionary A-Life Biological Model Department Of Computer Science Nottingham University, 2002 [DP97] Devine, P., and Paton, R.C. - Simulated evolution - Handbook of Evolutionary Computation release 97/1, 1997 - IOP Publishing Ltd and Oxford University Press [GPL97] Gracias, N., Pereira, H., Lime, J.A., and Rosa, A. - Gaia: An Artificial Life Environment for Ecological Systems Simulation Artificial Life V: pp. 124-134, 1997 MIT Press [Ham98] Hammer, O. Computer Simulation of the Evolution of Foraging Strategies: Application tot the Ichnological Record - Paleontologia Electronica Vol. 1, Issue 2, 1998 Palaeo-electronica.org [KU99] Komosinski, M., and Ulatowski, S. - Framsticks: Towards a Simulation of a Nature-like World, Creatures and Evolution Proceedings of the 5th European Conference on Artificial Life: pp. 261-265, 1999 Springer-Verslag Berlin Heidelberg [Kom00] Komosinski, M. - The World of Framsticks: Simulation, Evolution, Interaction VW2000, LNAI 1834: pp. 214-224, 2000 - Springer-Verslag Berlin Heidelberg [LCY04] Lee, M., Chang, O., Yoo, C., Kim, Y.S., and Gwun, O.U. Behavior Evolution of Multiple Mobile Agents under Solving a Continuous Pursuit Using Artificial Life Concept Journal of Intelligent and Robotic Systems 39: pp. 433-445, 2004 Kluwer Academic Publishers B.V. [Mar00] Marrow, P. - Nature-inspired computing technology and applications BT Technology Journal: pp. 13-23, 2000 Springer Science+Business Media B.V., Formerly Kluwer Academic Publishers B.V. [May01] Mayr, E. How are evolutionary change and adaptedness explained? & Origin and evolution of diversity: Cladogenesis - What Evolution Is: pp. 73-232, 2001 - Basic Books, New York [Mil95] Miller, G.F. Artificial Life as Theoretical Biology: How to do real science with computer simulation - CSRP 378. Tech. rep., School of Cognitive and Computing Sciences, 1995 - Sussex University [MF95] Mitchel, M., and Forrest, S. - Genetic Algorithms and Artificial Life Artificial Life I: pp. 267-289, 1995 MIT Press [PSM99] Papadopouls, D., Schneide, D., Meier-Eis, J., Arber, W., Lenski, R.E. Genomic evolution during a 10,000- generations experiment with bacteria Proceedings of the National Academy of Sciences of the USA, vol. 96, issue 7: pp. 3807-3812, 1999 [Ray91] Ray, T.S. An approach to the Synthesis of Life Artificial Life II, SFI Studies in the Science of Complexity, vol. X: pp. 371-408, 1991 - CA: Addison- Wesley [Yea94] Yaeger, L. Computational Genetics, Physiology, Metabolism, Neural Systems, Learning, Vision, and Behavior or PolyWorld: Life in a New Context Artificial Life III: pp. 263-298, 1994 - MA: Addison- Wesley