Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
An Analysis of the Causes of Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Non-destructive Depth-Dependent Crossover for Genetic Programming
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
On the constructiveness of context-aware crossover
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic programming for medical classification: a program simplification approach
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Automatically evolving rule induction algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
| Hi-index | 0.00 |
Genetic Programming (GP) provides a novel way of classification with key features like transparency, flexibility and versatility. Presence of these properties makes GP a powerful tool for classifier evolution. However, GP suffers from code bloat, which is highly undesirable in case of classifier evolution. In this paper, we have proposed an operator named ''DepthLimited crossover''. The proposed crossover does not let trees increase in complexity while maintaining diversity and efficient search during evolution. We have compared performance of traditional GP with DepthLimited crossover GP, on data classification problems and found that DepthLimited crossover technique provides compatible results without expanding the search space beyond initial limits. The proposed technique is found efficient in terms of classification accuracy, reduced complexity of population and simplicity of evolved classifiers.