Reducing Bloat and Promoting Diversity using
Multi-Objective Methods
Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001), page 11--18. San Francisco, California, USA, Morgan Kaufmann, (7-11 July 2001)Abstract
Two important problems in genetic programming (GP) are
its tendency to find unnecessarily large trees (bloat),
and the general evolutionary algorithms problem that
diversity in the population can be lost prematurely.
The prevention of these problems is frequently an
implicit goal of basic GP. We explore the potential of
techniques from multi-objective optimization to aid GP
by adding explicit objectives to avoid bloat and
promote diversity. The even 3, 4, and 5-parity problems
were solved efficiently compared to basic GP results
from the literature. Even though only non-dominated
individuals were selected and populations thus remained
extremely small, appropriate diversity was maintained.
The size of individuals visited during search
consistently remained small, and solutions of what we
believe to be the minimum size were found for the 3, 4,
and 5-parity problems.