This paper develops a notion of quasi distance between fuzzy sets (rule detectors) that preserves a notion of fuzzy set (rule) dominance. Such quasi distance notion is used by an evolutionary algorithm during its deterministic crowding mechanism in order to preserve diversity of the evolved fuzzy rule detectors. Moreover, the evolutionary process uses a variable length encoding that allows to tune up the fuzzy set associated to each attribute in the atomic condition of a fuzzy rule by evolving the fuzzy set parameters. Experiments with real data sets are performed and some results are reported.
['Jonatan Piedra Gomez', 'Elizabeth León']
A Fuzzy Set/Rule Distance for Evolving Fuzzy Anomaly Detectors