1,363 research outputs found

    Critical mutation rate has an exponential dependence on population size in haploid and diploid populations

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    Understanding the effect of population size on the key parameters of evolution is particularly important for populations nearing extinction. There are evolutionary pressures to evolve sequences that are both fit and robust. At high mutation rates, individuals with greater mutational robustness can outcompete those with higher fitness. This is survival-of-the-flattest, and has been observed in digital organisms, theoretically, in simulated RNA evolution, and in RNA viruses. We introduce an algorithmic method capable of determining the relationship between population size, the critical mutation rate at which individuals with greater robustness to mutation are favoured over individuals with greater fitness, and the error threshold. Verification for this method is provided against analytical models for the error threshold. We show that the critical mutation rate for increasing haploid population sizes can be approximated by an exponential function, with much lower mutation rates tolerated by small populations. This is in contrast to previous studies which identified that critical mutation rate was independent of population size. The algorithm is extended to diploid populations in a system modelled on the biological process of meiosis. The results confirm that the relationship remains exponential, but show that both the critical mutation rate and error threshold are lower for diploids, rather than higher as might have been expected. Analyzing the transition from critical mutation rate to error threshold provides an improved definition of critical mutation rate. Natural populations with their numbers in decline can be expected to lose genetic material in line with the exponential model, accelerating and potentially irreversibly advancing their decline, and this could potentially affect extinction, recovery and population management strategy. The effect of population size is particularly strong in small populations with 100 individuals or less; the exponential model has significant potential in aiding population management to prevent local (and global) extinction events

    Understanding sports violence: revisiting foundational explorations

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    Within this paper we discuss the importance of attending to definitions of ‘violence’. Through a return to a selection of important foundational works, we attempt to unpack the fundamental meanings of violence in a general sense, and sport violence in particular. With a specific focus on the need for definitional clarity, and particular attention to the ‘ritual’ dimensions of sport violence, we argue that engaging with these concepts is essential when conducting research in ‘violent’ contexts. Based on a critical reading of a small selection of relatively recent scholarship in sports settings, we ultimately argue that without careful consideration of what can constitute ‘violence’, scholars risk misrepresenting the social worlds they investigate. In conclusion we call for researchers to enter into a dialogue with foundational explorations of violence and to pays far greater heed to the definitions favoured by practitioners who engage with apparent ‘violence’ on a regular basis

    The N-strikes-out algorithm: A steady-state algorithm for coevolution

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    We introduce the N-strikes-out algorithm, a simple steady-state genetic algorithm for competitive coevolution. The algorithm can be summarised as follows: Run competitions between randomly chosen individuals, keep track of the number of defeats for each individual, and remove any individual which has been defeated N times. Naive application of the algorithm in 2-population problems leads to severe disengagement. We find that disengagement can be eliminated (for all tasks involving real-valued continuous scores) by determining 'victories' and 'defeats' between fellow members of the same species, using competitions against a single member of the opposing species as a point of comparison. We apply our algorithm to the "box-grabbing" problem for artificial 3D creatures introduced by Sims. We compare our algorithm with Sims' original Last Elite Opponent algorithm, and describe (and explain) different results obtained with two different implementations differing mainly by the harshness of their selection regimes

    What makes a 'good group'? Exploring the characteristics and performance of undergraduate student groups

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    Group work forms the foundation for much of student learning within higher education, and has many educational, social and professional benefits. This study aimed to explore the determinants of success or failure for undergraduate student teams and to define a ‘good group’ through considering three aspects of group success: the task, the individuals, and the team. We employed a mixed methodology, combining demographic data with qualitative observations and task and peer evaluation scores. We determined associations between group dynamic and behaviour, demographic composition, member personalities and attitudes towards one another, and task success. We also employed a cluster analysis to create a model outlining the attributes of a good small group learning team in veterinary education. This model highlights that student groups differ in measures of their effectiveness as teams, independent of their task performance. On the basis of this, we suggest that groups who achieve high marks in tasks cannot be assumed to have acquired team working skills, and therefore if these are important as a learning outcome, they must be assessed directly alongside the task output
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