  Most of the problems in genetic algorithms are very complex and demand a
large amount of resources that current technology can not offer. Our purpose
was to develop a Java-JINI distributed library that implements Genetic
Algorithms with sub-populations (coarse grain) and a graphical interface in
order to configure and follow the evolution of the search. The sub-populations
are simulated/evaluated in personal computers connected trough a network,
keeping in mind different models of sub-populations, migration policies and
network topologies. We show that this model delays the convergence of the
population keeping a higher level of genetic diversity and allows a much
greater number of evaluations since they are distributed among several
computers compared with the traditional Genetic Algorithms.
