- author = "Markus Brameier and Wolfgang Banzhaf",
- title = "A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2001",
- volume = "5",
- number = "1",
- pages = "17--26",
- month = feb,
- keywords = "genetic algorithms, genetic programming, Data mining, evolutionary computation, neural networks",
-
URL = "
http://web.cs.mun.ca/~banzhaf/papers/ieee_taec.pdf",
- size = "10 pages",
- abstract = "We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.",
-
notes = "proben1/UCI LGP variable length string of C
instruction. Branching. steady state tournament
selection. two-point string crossover {"}high mutation
rates have been experienced to produced better
results{"} p19. Size<=256 {"}it is much easier for the
GP system to implement structural introns [than
semantic ones]{"} p20 {"}for all problems discussed,
the performance of GP in generalization comes close to
or even better then the results documented for NNs{"}
(MLP, RPROP) p21 Ten demes of 500 connected in one
direction circle. 5% mutation rate. {"}On average, the
number of effective generations is reduced by a factor
of three when using demes. Tests with and without
conditionals. Runtime comparison.
Intron removal (dead code) at run time.",
