Genetic programming has highlighted the problem of bloat, the uncontrolled growth of the average size of an individual in the population. The most common approach to dealing with bloat in tree-based genetic programming individuals is to limit their maximal allowed depth. An alternative to depth limiting is to punish individuals in some way based on excess size, and our experiments have shown that the combination of depth limiting with such a punitive method is generally more effective than either alone. Which such combinations are most effective at reducing bloat? In this article we augment depth limiting with nine bloat control methods and compare them with one another. These methods are chosen from past literature and from techniques of our own devising. esting with four genetic programming problems, we identify where each bloat control method performs well on a per-problem basis, and under what settings various methods are effective independent of problem. We report on the results of these tests, and discover an unexpected winner in the cross-platform category.

The evolutionary computation community has shown increasing interest in arbitrary-length representations, particularly in the field of genetic programming. A serious stumbling block to the scalability of such representations has been bloat : uncontrolled genome growth during an evolutionary run. Bloat appears across the evolutionary computation spectrum, but genetic programming has given it by far the most attention. Most genetic programming models explain this phenomenon as a result of the growth of introns , areas in an individual which serve no functional purpose. This paper presents evidence which directly contradicts intron theories as applied to tree-based genetic programming. The paper then uses data drawn from this evidence to propose a new model of genome growth. In this model, bloat in genetic programming is a function of the mean depth of the modification (crossover or mutation) point. Points far from the root are correspondingly less likely to hurt the child's survivability in the next generation. The modification point is in turn strongly correlated to average parent tree size and to removed subtree size, both of which are directly linked to the size of the resulting child.