A comparison of linear genetic programming and neural networks in medical data mining

IEEE Transactions on Evolutionary Computation

Volumn 5, Issue 1, 2001, Pages 17-26

  Brameier, M ^a   Banzhaf, W ^a  

^a TU DORTMUND UNIVERSITY   (Germany)

Author keywords

Data mining; Evolutionary computation; Genetic programming; Neural networks

Indexed keywords

DATA MINING; DATABASE SYSTEMS; GENETIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINEAR PROGRAMMING; MEDICAL APPLICATIONS; NEURAL NETWORKS;

EVOLUTIONARY COMPUTATION; LINEAR GENETIC PROGRAMMING; MEDICAL DATA MINING;

EVOLUTIONARY ALGORITHMS;

EID: 0035247432     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/4235.910462     Document Type: Article

References (29)

1. Parallel genetic programming: A scalable implementation using the transputer network architecture
2. Genetic programming for pedestrians
3. Applications of artificial neural networks to clinical medicine
4. UCI repository of machine learning databases
5. Parallel machine code genetic programming
6. A representation for the adaptive generation of simple sequential programs
7. A learning machine-Part I
8. A learning machine, part II
9. Genetic programming for classification of brain tumours from nuclear magnetic resonance biopsy spectra
10. The stepping stone model of population structure and the decrease of genetic correlation with distance
11. Hierarchical genetic algorithms operating on populations of computer programs
12. Using grammar based genetic programming for data mining of medical knowledge
13. A compiling genetic programming system that directly manipulates the machine-code
14. Evolving turing-complete programs for a register machine with self-modifying code
15. Explicitly defined introns and destructive crossover in genetic programming
16. Evolutionary program induction of binary machine code and its applications
17. Proben 1-a set of neural network benchmark problems and benchmarking rules
18. A direct adaptive method for faster back-propagation learning: The RPROP algorithm
19. Neural networks as statistical methods in survival analysis
20. Computerized consensus diagnosis-A classification strategy for the robust analysis of MR spectra. 1. Application to H-1 spectra of thyroid neoplasma
21. Recombination, selection and the genetic construction of computer programs
22. Distributed genetic algorithms
23. Isolation by distance