### Use of Domain Information to Improve the Performance of an Evolutionary Algorithm

Abstract

In this thesis we explore the use of domain information incorporated during
the execution of an evolutionary algorithm, through the use of a cultural
algorithm. The cultural algorithms are evolutionary algorithms that
support an additional mechanism for information extraction during the
execution of the algorithm, avoiding the need to encode the information a
priori.
Firstly, a cultural algorithm to tackle constrained optimization problems
was developed. Such algorithm adopts differential evolution as its
model for the population. Using the differential evolution operators as a
base, we designed four knowledge sources, each one with a particular influence
over the operators. Since each knowledge source exhibits different
benefits in different phases of the search, a main mechanism to control the
application rate of the operators was developed, based on the success rate
each one.
This algorithm was tested using a well-known benchmark and a pair
of instances of engineering optimization problems, and compared with
other representative algorithms of the state-of-the-art. In both cases, equal
or better solutions were obtained, requiring a smaller number of objective
function evaluations.
In the next phase, a hybrid algorithm to tackle multiobjective optimization
problems was developed. Such algorithm is a hybrid between the
previous algorithm for constrained optimization, and a method of mathematical
programming called "-constraint. We obtained other advantages
with this algorithm, like obtaining good approaches of the Pareto front
in problems that have been very difficult to solve for other evolutionary
approaches.
As a last contribution, we introduced an approach to perform incor-
poration of preferences to the previous algorithm, but such approach can
also be used in an wide set of techniques. This proposal is based on the
use of vectors of goals. With the addition this approach, is possible to reduce
the computational cost needed when applying the hybrid algorithm
on problems with a large number of objectives, turning it applicable on
practice.