The parsimony pressure method is perhaps the simplest and most frequently used method to control bloat in genetic programming (GP). In this chapter we first reconsider the size evolution equation for genetic programming developed in Poli andMcPhee (Evol Comput 11(2):169-206, 2003) and rewrite it in a form that shows its direct relationship to Price’s theorem. We then use this new formulation to derive theoretical results that show how to practically and optimally set the parsimony coefficient dynamically during a run so as to achieve complete control over the growth of the programs in a population. Experimental results confirm the effectiveness of the method, as we are able to tightly control the average program size under a variety of conditions. These include such unusual cases as dynamically varying target sizes so that the mean program size is allowed to grow during some phases of a run, while being forced to shrink in others.
|Original language||English (US)|
|Title of host publication||Natural Computing Series|
|Number of pages||24|
|State||Published - 2014|
|Name||Natural Computing Series|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2014.