Andrew Tuson

Learn More
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)
In recent years, research into ‘neighbourhood search’ optimisation techniques such as simulated annealing, tabu search, and evolutionary algorithms has increased apace, resulting in a number of useful heuristic solution procedures for real-world and research combinatorial and function optimisation problems. Unfortunately, their selection and design remains(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 presents a heuristic for directing the neighbourhood (mutation operator) of stochastic optimisers, such as evolutionary algorithms, so to improve performance for the owshop sequencing problem. Based on idle time, the heuristic works on the assumption that jobs that have to wait a relatively long time between machines are in an unsuitable position(More)
Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in(More)