- author = "James P. Hoffmann and Christopher D. Ellingwood and Osei M. Bonsu and Daniel E. Bentil",
- title = "Ecological Model Selection via Evolutionary Computation and Information Theory",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2004",
- volume = "5",
- number = "2",
- pages = "229--241",
- month = jun,
- keywords = "genetic algorithms, genetic programming, model selection, parsimony, complexity-based fitness, variable-length representation",
- ISSN = "1389-2576",
-
DOI = "
10.1023/B:GENP.0000023690.71330.42",
- abstract = "an evolutionary algorithm-based approach to model selection and demonstrates its effectiveness in using the information content of ecological data to choose the correct model structure. Experiments with a modified genetic algorithm are described that combine parsimony with a novel gene regulation mechanism. This combination creates evolvable switches that implement functional variable-length genomes in the GA that allow for simultaneous model selection and parameter fitting. In effect, the GA orchestrates a competition among a community of models. Parsimony is implemented via the Akaike Information Criterion, and gene regulation uses a modulo function to overload the gene values and create an evolvable binary switch. The approach is shown to successfully specify the correct model structure in experiments with a nested set of polynomial test models and complex biological simulation models, even when Gaussian noise is added to the data.",
-
notes = "Special Issue on Biological Applications of Genetic
and Evolutionary Computation Guest Editor(s): Wolfgang
Banzhaf , James Foster
(1) Botany, University of Vermont, Burlington, VT, 05405-0086
(2) Mathematics & Statistics, University of Vermont, Burlington, VT, 05401-0086-3357",
