In this paper we use Genetic Programming for the classification of different seafloor habitats, based on the acoustic backscatter data from an echo sounder. By developing a different fitness function and dividing the multiple-class problem into several two-class problems, we were able to improve the results presented in a previously published work, providing a better discrimination between most of the seafloor types used in this study. We discuss the quality of these results and provide ideas to further improve the classification performance.

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