Variants of genetic programming for species distribution modelling — fitness sharing, partial functions, population evaluation

We investigate the use of partial functions, fitness sharing and committee learning in genetic programming. The primary intended application of the work is in learning spatial relationships for ecological modelling. The approaches are evaluated using a well-studied ecological modelling problem, the greater glider population density problem. Combinations of the three treatments (partial functions, fitness sharing and committee learning) are compared on the dimensions of accuracy and computational cost. Fitness sharing significantly improves learning accuracy, and populations of partial functions substantially reduce computational cost. The results of committee learning are more equivocal, and require further investigation. The learned models are highly predictive, but also highly explanatory.

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