GAGS library, or the application generator, has been applied in its previous versions to a variety of problems.

**Optimization of a fuzzy classifier**- In this application,
[Rojas, 1996][Rojas et al., 1996], the fuzzy rules used by a controller
are optimized. The linguistic values of the membership functions and
the fuzzy rules conclusions are coded, and optimal values are found in
an efficient way. GAGS allowed us to develop this application in
a minimal amount of time; basically, only the fitness function had to
be coded.
**Optimal test pattern generation**- Pattern generation can be
modelled as an optimization problem in which the target is to
determine a test signal that maximizes the quadratic difference
between the nominal response of a circuit and the faulty one due to a
defect. This application, presented in [Bernier et al., 1995] was also programmed in very few hours, and
results obtained improved those obtained by the other algorithms
tested: simulated annealing, a heuristic algorithm and a Hopfield
neural network. Obviously, since many solutions are tackled at the
same time, it takes much more time than any of them. This program has
been included in the GAGS release as an example for the
application generator.
**Search for a solution in the Mastermind game**- Playing Mastermind
is a search problem, in which the player must find a hidden
combination set by the oponent. The player lays a combination of
tokens, and the oponent answers with the number of tokens in a correct
position, without poiting out which ones they are, and the number of
tokens with correct color. The player must find the correct
combination based on these hints. In this case, presented in [Bernier et al., 1996], the GA looks for a
combination that meets the restrictions made so far by the oponent,
and plays it. It manages to play better than other algorithms, like
simulated annealing and a random search algorithm, without the need to
examine all the search space; besides, its performance does not
degrade significantly with the size of the problem; the number of
combinations needed to find the correct one increases roughly linearly
with the size of the search space. This application uses fully the
capabilities of the library; since it is a complex problem, the
application generator could not be used. Besides, some special-purpose
operators had to be applied.
**Job shop scheduling**- Production scheduling is a problem of
optimizing the time and resources needed to accomplish a set of tasks
while, at the same time, satisfying some constraints. In this work,
published in this same proceedings [Gutiérrez et al., 1996], GAs are used to
optimize the
*makespan*of a set of operations. GAGS had to be adapted extensively to use repair operators.

`jmerelo@kal-el.ugr.es`

Equipo GeNeura -- GeNeura Team

Departamento de Electrónica y Tecnología de los Computadores

Universidad de Granada Granada (Espaņa)

Phone: +34-58-243162; Fax: +34-58-243230