Evolving complex fuzzy classifier rules using a linear tree genetic representation
Dipankar Dasgupta, Fabio A. González · 2001
The paper proposes a linear representation of tree structures in order to evolve complex fuzzy rule sets for solving classification problems. In particular, linguistic rules are evolved, where the condition part of a rule can have an arbitrary structure of conjunctions and disjunctions. We describe an efficient rule representation scheme, which uses a genetic algorithm. The method is tested with a number of benchmark data sets and some results are reported.