Genetic programming with active data selection

Abstract

Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with gen-erality makes genetic programming an interesting tool for automatic pro-gramming and machine learning. One weakness is the enormous time re-quired for evolving complex programs. In this paper we present a method for accelerating evolution speed of genetic programming by active selec-tion of fitness cases during the run. In contrast to conventional genetic programming in which all the given training data are used repeatedly, the presented method evolves programs using only a subset of given data chosen incrementally at each generation. This method is applied to the evolution of collective behaviors for multiple robotic agents. Exper-imental evidence supports that evolving programs on an incrementally selected subset of finess cases can significantly reduce the fitness eval-uation time without sacrificing generalization accuracy of the evolved programs.