Genetic Algorithms (GA) is a family of search algorithms based
on the mechanics of natural selection and biological evolution. They are
able to efficiently exploit historical information in the evolution process to
look for optimal solutions or approximate them for a given problem, achieving
excellent performance in optimization problems that involve a large
set of dependent variables. Despite the excellent results of GAs, their use
may generate new problems. One of them is how to provide a good fitting
in the usually large number of parameters that must be tuned to allow a
good performance.
This paper describes a new platform that is able to extract the Regular
Expression that matches a set of examples, using a supervised learning
and agent-based framework. In order to do that, GA-based agents decompose
the GA execution in a distributed sequence of operations performed
by them. The platform has been applied to Language induction problem,
for that reason the experiments are focused on the extraction of the regular
expression that matches a set of examples. Finally, the paper shows
the efficiency of the proposed platform (in terms of fitness value) applied
to three case studies: emails, phone numbers and URLs. Moreover, it is
described how the codification of the alphabet affects to the performance
of the platform.This work has been partially supported by the Spanish Ministry of Science
and Innovation under the projects COMPUBIODIVE(TIN2007-65989), V-LeaF
(TIN2008-02729-E/TIN), Castilla-La Mancha project PEII09-0266-6640 and HADA
(TIN2007-64718)