- author = "Stefano Cagnoni and Federico Bergenti and Monica Mordonini and Giovanni Adorni",
- title = "Evolving Binary Classifiers Through Parallel Computation of Multiple Fitness Cases",
- journal = "IEEE Transactions on Systems, Man and Cybernetics - Part B",
- year = "2005",
- volume = "35",
- number = "3",
- pages = "548--555",
- month = jun,
- email = "cagnoni@ce.unipr.it",
- keywords = "genetic algorithms, genetic programming, cellular programming, sub-machine code genetic programming, multiple classifiers, pattern recognition",
- ISSN = "1083-4419",
-
DOI = "
doi:10.1109/TSMCB.2005.846671",
- size = "8 pages",
-
abstract = "We describe two versions of a novel approach to
developing binary classifiers, based on two
evolutionary computation paradigms: cellular
programming and genetic programming. Such an approach
achieves high computation efficiency both during
evolution and at runtime. Evolution speed is optimised
by allowing multiple solutions to be computed in
parallel. Runtime performance is optimized explicitly
using parallel computation in the case of cellular
programming or implicitly, taking advantage of the
intrinsic parallelism of bitwise operators on standard
sequential architectures in the case of genetic
programming.
The approach was tested on a digit recognition problem and compared to a reference classifier.",
- notes = "PMID: 15971922 [PubMed - indexed for MEDLINE]",
