Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
PADO: a new learning architecture for object recognition
Symbolic visual learning
Two self-adaptive crossover operators for genetic programming
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
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Generation of Structured Process Models Using Genetic Programming
Selected Papers from AISB Workshop on Evolutionary Computing
Genetic programming: profiling reasonable parameter value windows with varying problem difficulty
International Journal of Innovative Computing and Applications
An improved representation for evolving programs
Genetic Programming and Evolvable Machines
On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Autoconstructive evolution for structural problems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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In this work we tried to reduce the number of free parameters within Genetic Programming without reducing the quality of the results. We developed three new methods to adapt the probabilities, different genetic operators are applied with. Using two problems from the areas of symbolic regression and classification we showed that the results in these cases were better than randomly chosen parameter sets and could compete with parameter sets chosen with empirical knowledge.