A Dynamic Programming Approach to Individual Initialization in Genetic Programming

In this paper we present a new initialization method for genetic programming based on randomized exhaustive enumeration. It naturally enables complete sharing of sub trees among individuals which in turn allows an efficient reuse of computations. Moreover, it can be implemented as a random one pass initialization. We present experimental results on different instances of simple symbolic regression exploring the landscape of possible initializations based on our approach and confirming the usability of these initializations.