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- Andrew Tuson, Peter Ross
- Evolutionary Computation
- 1998

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)

We describe a series of experiments in generating traditional musical harmony using Genetic Algorithms. We discuss some problems which are specific to the musical domain, and conclude that a GA with no notion of meta-level control of the reasoning process is unlikely to solve the harmonisation problem well.

- Andrew Tuson, Peter Ross
- PPSN
- 1996

In the vast majority of genetic algorithm implementations, the operator probabilities are xed throughout a given run. However, it may be useful to adjust these probabilities during the run, according to the ability of the operators to produce children of increased tness. Cost Based Operator Rate Adaptation (COBRA) periodically re-ranks operator… (More)

- Hugh M. Cartwright, Andrew Tuson
- Evolutionary Computing, AISB Workshop
- 1994

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)

- Andrew Tuson
- 1999

Dynamic optimiser design currently assumes that diversity is a desirable property towards achieving adaptability, as a population-based optimiser contains an implicit memory. This paper examines the applicability of this assumption. Population-based algorithms of different size are tested against optimisers using a single solution. Results presented here… (More)

- Richard Jensen, Andrew Tuson, Qiang Shen
- FUZZ-IEEE
- 2010

— 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)

- Richard Jensen, Qiang Shen, Andrew Tuson
- RSFDGrC
- 2005

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)

- Tao Gong, Andrew L. Tuson
- 2007

Particle Swarm Optimization (PSO) is an innovative and competitive optimization technique for numerical optimization with real-parameter representation. This paper examines the working mechanism of PSO in a principled manner with forma analysis and investigates the applicability of PSO on the Quadratic Assignment Problem (QAP). Particularly, the derived PSO… (More)