Illinois Genetic Algorithms Laboratory

Llorà, X., Wilson, S. W. (2004)
TR No.: 2004014 | Download PDF | Download PS

Abstract:
Learning classifier systems tend to inherit—a priori—a given
knowledge representation language for expressing the concepts to learn.
Hence, even before getting started, this choice biases what can be learned,
becoming critical for some real-world applications like data mining.
However,
such bias may be minimized by hybridizing different knowledge
representations
via evolutionary mixing. This paper presents a first attempt to produce
an evolutionary framework that evolves mixed decision trees of
heterogeneous
knowledge representations.