Abstract
This paper investigates the application of committee
learning to fitness-shared genetic programming.
Committee learning is applied to populations of either
partial and total functions, and using either fitness
sharing or raw fitness, giving four treatments in all.
The approaches are compared on three problems, the 6-
and 11-multiplexer problems, and learning recursive
list membership functions. As expected, fitness sharing
gave better performance on all problems than raw
fitness. The comparison between populations of partial
and total functions with fitness sharing is more
equivocal. The results are very similar, though
slightly in favour of total functions. However there
are strong indications that the average size of
individuals in the partial function populations are
smaller, and hence might be expected to generalise
better, though this was not investigated in this
paper
Users
Please
log in to take part in the discussion (add own reviews or comments).