Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Adaptive Penalties for Evolutionary Graph Coloring
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
A Superior Evolutionary Algorithm for 3-SAT
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Using EAs for Error Prediction in Near Infrared Spectroscopy
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
A Comparison of Genetic Programming Variants for Data Classification
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Characterizing the dynamics of symmetry breaking in genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic programming for classification with unbalanced data
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
| Hi-index | 0.00 |
In this paper we describe how the Stepwise Adaptation of Weights (saw) technique can be applied in genetic programming. The saw-ing mechanism has been originally developed for and successfully used in eas for constraint satisfaction problems. Here we identify the very basic underlying ideas behind saw-ing and point out how it can be used for different types of problems. In particular, saw-ing is well-suited for data mining tasks where the fitness of a candidate solution is composed by 'local scores' on data records. We evaluate the power of the saw-ing mechanism on a number of benchmark classification data sets. The results indicate that extending the gp with the saw-ing feature increases its performance when different types of misclassifications are not weighted differently, but leads to worse results when they are.