Abstract

In the last decades, engineers and decision makers expressed a growing interest in
the development of effective modeling and simulation methods to understand or
predict the behavior of many phenomena in science and engineering. Many of
these phenomena are translated in mathematical models for convenience and to carry
out an easy interpretation. Methods commonly employed for this purpose include, for
example, Neural Networks, Simulated Annealing, Genetic Algorithms, Tabu search,
and so on. These methods all seek for the optimal or near optimal values of a
predefined set of parameters of a model built a priori. But in this case, a suitable
model should be known beforehand. When the form of this model cannot be found,
the problem can be seen from another level where the goal is to find a program or a
mathematical representation which can solve the problem. According to this idea the
modeling step is performed automatically thanks to a quality criterion which drives
the building process.

In this thesis, we focus on the Genetic Programming (GP) approach as an automatic
method for creating computer programs by means of artificial evolution based upon
the original contributions of Darwin and Mendel.
While GP has proven to be a powerful means for coping with problems in which
finding a solution and its representation is difficult, its practical applicability is still
severely limited by several factors. First, the GP approach is inherently a stochastic
process. It means there is no guarantee to obtain a satisfactory solution at the end of
the evolutionary loop. Second, the performances on a given problem may be strongly
dependent on a broad range of parameters, including the number of variables involved,
the quantity of data for each variable, the size and composition of the initial
population, the number of generations and so on.
On the contrary, when one uses Genetic Programming to solve a problem, he has two
expectancies: on the one hand, maximize the probability to obtain an acceptable
solution, and on the other hand, minimize the amount of computational resources to
get this solution.
Initially we present innovative and challenging applications related to several fields in
science (computer science and mechanical science) which participate greatly in the
experience gained in the GP field.

Then we propose new strategies for improving the performances of the GP approach
in terms of efficiency and accuracy. We probe our approach on a large set of
benchmark problems in three different domains. Furthermore we introduce a new
approach based on GP dedicated to symbolic regression of multivariate data-sets
where the underlying phenomenon is best characterized by a discontinuous function.
These contributions aim to provide a better understanding of the key features and the
underlying relationships which make enhancements successful in improving the
original algorithm.