Modification point depth and genome growth in genetic programming.
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.
Luke
Sean
S
Department of Computer Science, George Mason University, 4400 University Drive MS# 4A5, Fairfax, VA 22030, USA. sean@cs.gmu.edu
D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D003434:Crossing Over, Genetic; D016678:Genome; D007438:Introns; D008957:Models, Genetic; D015233:Models, Statistical; D009154:Mutation