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Natural Coding: A More Efficient Representation for Evolutionary Learning*
Raúl Giráldez, Jesús S. Aguilar-Ruiz, and José C. Riquelme
Department of Computer Science,
University of Seville
Avenida Reina Mercedes s/n,
41012 Sevilla, Spain
{giraldez,aguilar,riquelme}@lsi.us.es
Abstract.
To select an adequate coding is one of the main problems in
applications based on Evolutionary Algorithms. Many codings have
been proposed to represent the search space for obtaining decision
rules. A suitable representation of the individuals of the genetic
population can reduce the search space, so that the learning
process is accelerated by decreasing the number of necessary
generations to complete the task. In this sense, natural coding
achieves such reduction and improves the results obtained by other
codings. This paper justifies the use of natural coding by
comparing it with hybrid coding that joins well-known binary and
real representations. We have tested both codings on a
heterogeneous subset of databases from the UCI Machine Learning
Repository. The experiments' results show that natural coding
improves the quality of the obtained knowledge-model using only
one third of the generations that hybrid coding needs as well as a
smaller population.
Keywords: Evolutionary Algorithms, Coding, Supervised Learning
*This research was supported by the Spanish Research Agency CICYT
under grant TIC2001-1143-C03-02.
LNCS 2723, p. 979 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003