Seth Bullock

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Whilst the rate at which mutations occur in artiicial evolutionary systems has received considerable attention, there has been little analysis of the mutation operators themselves. Here attention is drawn to the possibility that inherent biases within such operators might arte-factually aaect the direction of evolutionary change. Biases associated with(More)
Evolutionary simulation modelling is presented as a methodology involving the application of modelling techniques developed within the artificial sciences to evolutionary problems. Although modelling work employing this methodology has a long and interesting history, it has remained, until recently, a relatively underdeveloped practice, lacking a unifying(More)
Hurd's (1995) model of a discrete action-response game, in which the interests of sig-nallers and receivers connict, is extended to address games in which, as well as signal cost varying with signaller quality, the value of an ob-server's response to a signal is also dependent on signaller quality. It is shown analytically that non-handicap signalling(More)
Tononi [Proc. Natl. Acad. Sci. U.S.A. 91, 5033 (1994)] proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond. We develop an approximation of the measure for a popular Gaussian model which, applied to a(More)
Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In(More)
While standard evolutionary algorithms employ a static, absolute fitness metric, coevolutionary algorithms assess individuals by their performance relative to populations of opponents that are themselves evolving. Although this arrangement offers the possibility of avoiding long-standing difficulties such as premature convergence, it suffers from its own(More)
As a population evolves, its members are under selection both for rate of reproduction (fitness) and mutational robustness. For those using evolutionary algorithms as optimisation techniques, this second selection pressure can sometimes be beneficial, but it can also bias evolution in unwelcome and unexpected ways. Here, the role of selection for mutational(More)
An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics(More)
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A major challenge within open markets is the ability to satisfy service demand with an adequate supply of service providers, especially when such demand may be volatile due to changing requirements, or fluctuations in the availability of services. Ideally, this supply and demand should be balanced; however, when consumer demand changes over time, and(More)