abstract = "It has been observed previously that genetic
programming populations can collapse to all single node
trees when a parsimony measure (tree node count) is
used in a multiobjective setting. We have investigated
the circumstances under which this can occur for both
the 6-parity boolean learning task and a range of
benchmark machine learning problems. We conclude that
mutation is an important and we believe a hitherto
unrecognised factor in preventing population collapse
in multiobjective genetic programming; without mutation
we routinely observe population collapse. From
systematic variation of the mutation operator, we
conclude that a necessary condition to avoid collapse
is that mutation produces, on average, an increase in
tree sizes (bloating) at each generation which is then
counterbalanced by the parsimony pressure applied
during selection. Finally, we conclude that the use of
a genotype diversity preserving mechanism is
ineffective at preventing population collapse.",
notes = "GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).