Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
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
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
Proceedings of the 3rd International Conference on Genetic Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Adapting the Fitness Function in GP for Data Mining
Proceedings of the Second European Workshop on Genetic Programming
An Improved Gene Expression Programming for Fuzzy Classification
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Genetic Programming for Image Recognition: An LGP Approach
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Program simplification in genetic programming for object classification
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
GP for object classification: brood size in brood recombination crossover
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Hi-index | 0.00 |
In this paper we report the results of a comparative study on different variations of genetic programming applied on binary data classification problems. The first genetic programming variant is weighting data records for calculating the classification error and modifying the weights during the run. Hereby the algorithm is defining its own fitness function in an on-line fashion giving higher weights to 'hard' records. Another novel feature we study is the atomic representation, where `Booleanization' of data is not performed at the root, but at the leafs of the trees and only Boolean functions are used in the trees' body. As a third aspect we look at generational and steady-state models in combination of both features.