abstract = "In essence, data mining consists of extracting
knowledge from data. This paper proposes a
co-evolutionary system for discovering fuzzy
classification rules. The system uses two evolutionary
algorithms: a genetic programming (GP) algorithm
evolving a population of fuzzy rule sets and a simple
evolutionary algorithm evolving a population of of
membership function definitions. The two populations
co-evolve, so that the final result of the
co-evolutionary process is a fuzzy rule set and a set
of membership function definitions which are well
adapted to each other. In addition, our system also has
some innovative ideas with respect to the encoding of
GP individuals representing rule sets. The basic idea
is that our individual encoding scheme incorporates
several syntactical restrictions that facilitate the
handling of rule sets in disjunctive normal form. We
have also adapted GP operators to better work with the
proposed individual encoding scheme.",
notes = "Broken Feb 2019
http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/index.html
PKDD-2001 Broken Feb 2019
http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42534-9