Andrew Tuson

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In the majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has been argued that these settings should vary over the course of a genetic algorithm run--so as to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an investigation into(More)
We discuss the use of genetic algorithms (GAs) for the generation of music. We explain the structure of a typical GA, and outline existing work on the use of GAs in computer music. We propose that the addition of domain-specific knowledge can enhance the quality and speed of production of GA results, and describe two systems which exemplify this. However,(More)
Scheduling in chemical flowshops is one of a number of important industrial problems which are potentially amenable to solution using the genetic algorithm. However the problem is not trivial: flowshops run continuously, and for efficient operation those controlling them must be able to adjust the order in which products are made as new requests are(More)
— This paper describes a novel, principled approach to real-valued dataset reduction based on fuzzy and rough set theory. The approach is based on the formulation of fuzzy-rough discernibility matrices, that can be transformed into a satisfiability problem; an extension of rough set approaches that only apply to discrete datasets. The fuzzy-rough hybrid(More)
The ability to obtain multiple distinct solutions in a single run is an important, though often forgotten, practical advantage of genetic algorithms, and therefore methods that enhance this ability are desirable. The approach taken here is inspired by nature: sexually reproducing organisms do not mate indiscriminately | the choice of mate has a large impact(More)