What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming

This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GP-hard. We indicate that, at least for this problem, the metaphor is misleading.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45613/1/10710_2004_Article_335714.pd

Visualizing Tree Structures in Genetic Programming

This paper presents methods to visualize the structure of trees that occur in genetic programming. These methods allow for the inspection of structure of entire trees even though several thousands of nodes may be involved. The methods also scale to allow for the inspection of structure for entire populations and for complete trials even though millions of nodes may be involved. Examples are given that demonstrate how this new way of “seeing” can afford a potentially rich way of understanding dynamics that underpin genetic programming. The examples indicate further studies that might be enabled by visualizing structure at these scales.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45620/1/10710_2005_Article_7621.pd

Towards Identifying Populations that Increase the Likelihood of

This paper presents a comprehensive, multivariate account of how initial population material is used over the course of a genetic programming run as while various factors influencing problem difficulty are changed. The results corroborate both theoretical and empirical studies on factors that influence population dynamics. The results also indicate a clue for a possible empirical measurement that could be used in tuning initial populations for increasing the likelihood of success

Limits to expression in genetic programming: Lattice-aggregate modeling

AbstreThis paper describes a general theoretical model of size and shape evolution in genetic programming. The proposed model incorporates a mekhanism that is analogous to ballistic accretion in physics. The model indicates a four-region partition of GP search space. It further suggests that two of these regions are not searchable by GP. I

Identifying Structural Mechanism in Standard GP

Abstract. This paper presents a hypothesis about an undiscovered class of mechanisms that exist in standard GP. Rather than being intentionally designed, these mechanisms would be an unintended consequence of using trees as information structures. A model is described that predicts outcomes in GP that would arise solely from such mechanisms. Comparisons with empirical results from GP lend support to the existence of these mechanisms.