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 influenc...
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...
Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this pap...
This paper presents a new tool for the automatic generation of highly parallelized Finite Impulse Response (FIR) filters. In this approach we follow our PARO design methodology. P...
Holger Ruckdeschel, Hritam Dutta, Frank Hannig, J&...
This paper describes a tunably-difficult problem for genetic programming (GP) that probes for limits to building block mixing and assembly. The existence of such a problem can be ...
Jason M. Daida, Michael E. Samples, Matthew J. Byo...