Fitness Landscapes and Inductive Genetic Programming

This paper proposes a study of the performance of inductive genetic programming with decision trees. The investigation concerns the influence of the fitness function, the genetic mutation operator and the categorical distribution of the examples in inductive tasks on the search process. The approach uses statistical correlations in order to clarify two aspects: the global and the local search characteristics of the structure of the fitness landscape. The work is motivated by the fact that the structure of the fitness landscape is the only information which helps to navigate in the search space of the inductive task. It was found that the analysis of the landscape structure allows tuning the landscape and increasing the exploratory power of the operator on this landscape.

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