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Titlebook: Applications of Evolutionary Computing; EvoWorkshops 2004: E Günther R. Raidl,Stefano Cagnoni,Giovanni Squiller Conference proceedings 2004

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Encyclopedia of Intensive Care Medicine we consider Manhattan Street Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of Manhattan. With the use of genetic programming we have generated broadcasting algorithms for 2-dimensi
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Abdominal Trauma, Damage Controle-state models of protocol entities from Message Sequence Charts. By converting the Message Sequence Charts into input/output sequences, the problem reduces to evolving finite-state machines with the specified input/output behaviors. The proposed approach does not overgeneralize the entity behavior
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Using Genetic Programming to Design Broadcasting Algorithms for Manhattan Street Networks we consider Manhattan Street Networks (MSNs) which are mesh-structured, toroidal, directed, regular networks such that locally they resemble the geographical topology of the avenues and streets of Manhattan. With the use of genetic programming we have generated broadcasting algorithms for 2-dimensional and 3-dimensional MSNs.
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Evolving Regular Expression-Based Sequence Classifiers for Protein Nuclear Localisations in the laboratory. For example, patterns representing nuclear localisation signals (NLSs) are used to predict nuclear localisation. NLSs are not yet well understood, and so the set of currently known NLSs may be incomplete. Here we use genetic programming (GP) to generate RE-based classifiers for
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