Genetic programming (GP) is a supervised learning method motivated by an analogy to biological evolution. GP creates successor hypotheses by repeatedly mutating and crossovering parts of the current best hypotheses, with expectation to find a good solution in the evolution process. In this report, the task to be performed was a symbolic regression problem, which is to find the symbolic function...

t has long been recognized, on the basis of numerous experimental and clinical studies, that the autonomic nervous system has a significant role in the pathogenesis of atrial fibril-lation (AF). More recent studies have focused on the ganglion-ated plexi (GP), which are part of an interconnected structural and functional atrial neural network constituting the intrinsic cardiac autonomic nervous...

In machine learning, reducing the complexity of a model can help to improve its computational efficiency and avoid overfitting. genetic programming (GP), reduction is often achieved by size evolved expressions. However, previous studies have demonstrated that expression does not necessarily prevent Therefore, this paper uses evaluation time – required evaluate GP on data as estimate complexity....