Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
Data mining tasks and methods: Classification: decision-tree discovery
Handbook of data mining and knowledge discovery
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The impact of population size on code growth in GP: analysis and empirical validation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
On the limiting distribution of program sizes in tree-based genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Operator equalisation and bloat free GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Crossover, sampling, bloat and the harmful effects of size limits
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Reassembling operator equalisation: a secret revealed
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Reassembling operator equalisation: a secret revealed
ACM SIGEVOlution
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
| Hi-index | 0.00 |
This paper compares Genetic Programming and Classification Trees on a problem of identification of burned areas in satellite imagery. Additionally, it studies how the most recently recognized bloat control technique, Operator Equalisation, affects the quality of the solutions provided by Genetic Programming. The merit of each approach is assessed not only by its classification accuracy, but also by the ability to predict the correctness of its own classifications, and the ability to provide solutions that are human readable and robust to data inaccuracies. The results reveal that both approaches achieve high accuracy with no overfitting, and that Genetic Programming can reveal some surprises and offer interesting advantages even on a simple problem so easily tackled by the popular Classification Trees. Operator Equalisation proved to be crucial.