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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)
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)
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)
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)
Bloom's taxonomy attempts to provide a set of levels of cognitive engagement with material being learned. It is usually presented as a generic framework. In this paper we outline some studies which examine whether the taxonomy is appropriate for computing, and how its application in computing might differ from its application elsewhere. We place this in the(More)
Genetic programming is a powerful technique for automatically generating program code from a description of the desired functionality. However it is frequently distrusted by users because the programs are generated with reference to a training set, and there is no formal guarantee that the generated programs will operate as intended outside of this training(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)