Will Smart

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This paper describes an approach to the use of gradient descent search in genetic programming for continuously evolving genetic programs for object classification problems. An inclusion factor is introduced to each node in a genetic program and gradient descent search is applied to the inclusion factors. Three new on-zero operators and two new continuous(More)
This paper describes an approach to the use of genetic programming for multi-class image recognition problems. In this approach, the terminal set is constructed with image pixel statistics, the function set consists of arithmetic and conditional operators, and the fitness function is based on classification accuracy in the training set. Rather than using(More)
We describe a semantically-based Web Map Mediation Service (WMMS) that allows researchers to define, capture and reuse semantic relationships between map categories, via a set of Web Services. A map can then be viewed according to different taxonomies or legends, whose categories have been semantically related to those native to the map. Such 'mappings' or(More)
The thesis will study behaviour and uses of fragment schemas in tree-based Genetic Programming (GP). Key contributions will include: new tools for efficient analysis of the propagation of fragment schemas in GP evolution, empirically based conclusions of the validity of the GP Building Block Hypothesis (GP-BBH) with fragment schemas and the accuracy of(More)
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