Strongly typed genetic programming
Evolutionary Computation
Genetic programming for epileptic pattern recognition in electroencephalographic signals
Applied Soft Computing
Texture segmentation by genetic programming
Evolutionary Computation
Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
CROS: A Contingency Response multi-agent system for Oil Spills situations
Applied Soft Computing
The unconstrained automated generation of cell image features for medical diagnosis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Networks of transform-based evolvable features for object recognition
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Genetic programming is used to evolve mineral identification functions for hyperspectral images. The input image set comprises 168 images from different wavelengths ranging from 428nm (visible blue) to 2507nm (invisible shortwave in the infrared), taken over Cuprite, Nevada, with the AVIRIS hyperspectral sensor. A composite mineral image indicating the overall reflectance percentage of three minerals (alunite, kaolnite, buddingtonite) is used as a reference or ''solution'' image. The training set is manually selected from this composite image, and results are cross-validated with the remaining image data not used for training. The task of the GP system is to evolve mineral identifiers, where each identifier is trained to identify one of the three mineral specimens. A number of different GP experiments were undertaken, which parameterized features such as thresholded mineral reflectance intensity and target GP language. The results are promising, especially for minerals with higher reflectance thresholds, which indicate more intense concentrations.