Article,
Variants of genetic programming for species distribution modelling -- fitness sharing, partial functions, population evaluation
Ecological Modelling, 146 (1-3): 231--241 (2001)
Abstract
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.
