Mike Shackleton

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In natural systems, the organism or phenotype is the result of a complex developmental process that is played out as the genetic information is interpreted. This is in stark contrast to many artificial evolutionary systems in which the phenotype is represented directly in the genetic information and there is no such development. As well as overcoming the(More)
It is unlikely that we can expect to apply traditional centralised management approaches to large-scale pervasive computing scenarios. Approaches that require manual intervention for system management will similarly not be sustainable in the context of future deployments considering their scale and their dynamic (or mobile) nature. This situation motivates(More)
We investigate applying an evolutionary algorithm (EA) to the design of a passive optical network (PON). We use three techniques to improve the performance. Firstly, to reduce the risk of sub-optimal convergence, we use a novel genetic encoding. Secondly, we combine the EA with a heuristic to guide the optimisation. Thirdly, we investigate various ways of(More)
In this chapter we compare the performance of two contrasting evolutionary algorithms addressing a similar problem, of information retrieval. The first, BTGP, is based upon genetic programming, while the second, MGA, is a genetic algorithm. We analyze the performance of these evolutionary algorithms through aspects of the evolutionary process they undergo(More)
The caddisfly genus Caenota Mosely 1953 (in Mosely & Kimmins 1953) currently contains 5 species known from eastern Australia. Caenota is distinguished from other Calocidae genera by having adult males with greatly expanded maxillary palpi and a large membranous process associated with the antennal scape. Of the 5 described species, the larvae of only 1 is(More)
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