In this paper we compare two different approaches for controlling bloat in Genetic Programming, tree depth limits and resource-limited GP. Tree depth limits operate at the individual level, avoiding excessive code growth by imposing a maximum depth to each individual. Resource-limited GP is a new technique that operates at the population level, limiting the total amount of resources the entire population can use. We compare their dynamics and performance on three problems: Symbolic Regression, Even Parity, and Artificial Ant. The results suggest that resource-limited GP is superior to tree depth limits, but we question this superiority and discuss possible ways of combining the strengths of both approaches, to further improve the results.
Keywords
genetic programming, code growth, bloat, tree depth limits, limited resources
Subject
Genetic Programming
Conference
2005 IEEE Congress on Evolutionary Computation, September 2005
Cited by
Year 2009 : 2 citations
Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39.
Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK.
Year 2008 : 2 citations
Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza).
Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia.
