- author = "Karl J. Brazier and Graeme Richards and Wenjia Wang",
- title = "Implicit Fitness Sharing Speciation and Emergent Diversity in Tree Classifier Ensembles",
- booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2004, 5th International Conference, Proceedings",
- year = "2004",
- pages = "333--338",
- editor = "Zheng Rong Yang and Richard M. Everson and Hujun Yin",
- publisher = "Springer",
- series = "Lecture Notes in Computer Science",
- volume = "3177",
- ISBN = "3-540-22881-0",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- address = "Exeter, UK",
- month = aug # " 25-27",
- organisation = "IEEE",
- keywords = "genetic algorithms, genetic programming, gene expression programming",
-
DOI = "
doi:10.1007/b99975",
- abstract = "Implicit fitness sharing is an approach to the stimulation of speciation in evolutionary computation for problems where the fitness of an individual is determined as its success rate over a number trials against a collection of succeed/fail tests. By fixing the reward available for each test, individuals succeeding in a particular test are caused to depress the size of one another's fitness gain and hence implicitly co-operate with those succeeding in other tests. An important class of problems of this form is that of attribute-value learning of classifiers. Here, it is recognised that the combination of diverse classifiers has the potential to enhance performance in comparison with the use of the best obtainable individual classifiers. However, proposed prescriptive measures of the diversity required have inherent limitations from which we would expect the diversity emergent from the self-organisation of speciating evolutionary simulation to be free. The approach was tested on a number of the popularly used real-world data sets and produced encouraging results in terms of accuracy and stability.",
-
notes = "http://www.dcs.ex.ac.uk/ideal04/
a) Cleveland heart data b) Thyroid data c) Pima Indians diabetes data d) E. coli data
",
