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A Variant Program Structure in Tree-Based Genetic Programming for Multiclass Object Classification

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Evolutionary Image Analysis and Signal Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 213))

Abstract

This chapter describes an approach to the use of genetic programming for multiclass object classification. Instead of using the standard tree-based genetic programming approach, where each genetic program returns just one floating point number that is then translated into different class labels, this approach invents a new program structure with multiple outputs, each for a particular class. A voting scheme is then applied to these output values to determine the class of the input object. The approach is examined and compared with the standard genetic programming approach on four multiclass object classification tasks with increasing difficulty. The results show that the new approach outperforms the basic approach on these problems. A characteristic of the proposed program structure is that it can easily produce multiple outputs for multiclass object classification problems, while still keeping the advantages of the standard genetic programming approach for easy crossover and mutation. This approach can solve a multiclass object recognition problem using a single evolved program in a single run.

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  1. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand

    Mengjie Zhang & Mark Johnston

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  1. Mengjie Zhang
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  2. Mark Johnston
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  1. Dipartimnto di Ingegneria dell’Informazione , Viale Usberti 181a, 43100, PARMA, Italy

    Stefano Cagnoni

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Zhang, M., Johnston, M. (2009). A Variant Program Structure in Tree-Based Genetic Programming for Multiclass Object Classification. In: Cagnoni, S. (eds) Evolutionary Image Analysis and Signal Processing. Studies in Computational Intelligence, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01636-3_4

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  • DOI: https://doi.org/10.1007/978-3-642-01636-3_4

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