PADO: a new learning architecture for object recognition
Symbolic visual learning
Evolving programmers: the co-evolution of intelligent recombination operators
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
VQ Score Normalisation for Text-dependent and Text-independent Speaker Recognition
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Autoconstructive evolution for structural problems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
It's fate: a self-organising evolutionary algorithm
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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In this contribution we investigate the evolution of operators for Genetic Programming by means of Genetic Programming. Metaevolution of recombination operators in graph-based GP is applied and compared to other methods for the variation of recombination operators in graph-based GP. We demonstrate that a straightforward application of recombination operators onto themselves does not work well. After introducing an additional level of recombination operators (the meta level) which are recombining a pool of recombination operators, even self-recombination on the additional level becomes feasible. We show that the overall performance of this system is better than in other variants of graph GP. As a test problem we use speaker recognition.