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Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to out-perform purely syntactic operators. In this work, using a formal geometric view on search(More)
Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better(More)
Bloom's taxonomy of the cognitive domain and the SOLO taxonomy are being increasingly widely used in the design and assessment of courses, but there are some drawbacks to their use in computer science. This paper reviews the literature on educational taxonomies and their use in computer science education, identifies some of the problems that arise, proposes(More)
Many data mining applications involve the task of building a model for predictive classification. The goal of such a model is to classify examples (records or data instances) into classes or categories of the same type. The use of variables (attributes) not related to the classes can reduce the accuracy and reliability of a classification or prediction(More)
Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the(More)
This paper introduces a novel technique for the visual-ization of data at various levels of detail. This is based on a colour-based representation of the data, where " high level " views of the data are obtained by merging colours together to obtain a summary-colour which represents a number of data-points. This is applied to the problem of visualizing(More)
Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with(More)
Exploring the sounds available from a synthesis algorithm is a complicated process, requiring the user either to spend much time gaining heuristic experience with the algorithm or requiring them to have a deep knowledge of the underlying synthesis algorithms. In this paper we describe a computer system which facilitates a more exploratory approach to sound(More)
This paper proposes a novel Ant Colony Optimi-sation algorithm (ACO) tailored for the hierarchical multi-label classification problem of protein function prediction. This problem is a very active research field, given the large increase in the number of uncharacterised proteins available for analysis and the importance of determining their functions in(More)