Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study

Abstract

This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not derive from its influence on the genetic pool diversity.

Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control

“…Others have proposed operators that work at the population level, such a selection strategy that eliminates larger trees (Fernández and Martín 2004;Luke and Panait 2006). A more recent example is the prune and plant method, an elaborate mutation operator inspired from agricultural practices (Alfaro-Cid et al 2009), which is similar to other specially designed mutation operators, such as hoist and shrink (Kinnear 1993;Angeline 1997). In the case of special forms of crossover, Langdon (2000) presents size fair (SFX) and homologous crossover (HX), two approaches that explicitly consider the size of the subtree that is removed from one parent when choosing which subtree to insert from the other.…”

Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control

“…Many techniques to control bloat have been proposed in the last two decades (for recent reviews see Luke and Panait, 2006;Silva and Costa, 2009;Alfaro-Cid et al, 2010)). One of these is the Tarpeian method introduced in .…”

“…The Tarpeian method has been used in a variety of studies and applications. For example, in (Mahler et al, 2005) its performance and generalisation capabilities were studied, while it was compared with other bloat-control techniques in (Luke and Panait, 2006;Wyns and Boullart, 2009;Alfaro-Cid et al, 2010). The method has been used with success in the evolution of bin packing heuristics (Burke et al, 2007;Allen et al, 2009), in the evolution of image analysis operators (Roberts and Claridge, 2004), in artificial financial markets based on GP (MartinezJaramillo and Tsang, 2009), just to mention a few.…”

Covariant Tarpeian Method for Bloat Control in Genetic Programming

“…Many techniques to control bloat have been proposed in the last two decades (for recent reviews see Luke and Panait, 2006;Alfaro-Cid et al, 2010;Silva, 2008)). One of the very few with a theoretically-sound basis is the Tarpeian method introduced in .…”

“…For example, Mahler et al (2005) studied its performance and generalisation capabilities, while it was compared with other bloat-control techniques in (Luke and Panait, 2006;Wyns and Boullart, 2009;Alfaro-Cid et al, 2010). The method has been used with success in the evolution of bin packing heuristics (Burke et al, 2007;Allen et al, 2009), in the evolution of image analysis operators (Roberts and Claridge, 2004), in artificial financial markets based on GP (Martinez-Jaramillo and Tsang, 2009), in predicting protein networks (Geffner et al, 2008), in the design of passive analog filters using GP (Chouza et al, 2009), in the prediction of proteinprotein functional associations (Garcia et al, 2008) and in the simplification of decision trees via GP (Garcia-Almanza and Tsang, 2006).…”