Authors: Mengjie Zhang, Will Smart
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We describe an approach to the use of genetic programming for multi-class object classification problems. Rather than using fixed static thresholds as boundaries to distinguish between different classes, this approach introduces two methods of classification where the boundaries between different classes can be dynamically determined during the evolutionary process. The two methods are centred dynamic class boundary determination and slotted dynamic class boundary determination. The two methods are tested on four object classification problems of increasing difficulty and are compared with the commonly used static class boundary method. The results suggest that, while the static class boundary method works well on relatively easy object classification problems, the two dynamic class boundary determination methods outperform the static method for more difficult, multiple class object classification problems. Keywords: Genetic programming, genetic algorithms, dynamic class boundary determination, object recognition.