Algorithmes evolutionnaires appliques a la reconnaissance des formes et a la conception optique
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@PhdThesis{gagne:thesis,
author = "Christian Gagne",
title = "Algorithmes evolutionnaires appliques a la
reconnaissance des formes et a la conception optique",
school = "Laval University",
year = "2005",
address = "Quebec (QC), Canada",
month = may,
keywords = "genetic algorithms, genetic programming",
URL = "
http://vision.gel.ulaval.ca/en/publications/Id_528/PublDetails.php",
URL = "
http://vision.gel.ulaval.ca/~cgagne/pubs/these-cgagne.pdf",
URL = "http://www.theses.ulaval.ca/2005/22701/22701.pdf",
size = "212 pages",
abstract = "Evolutionary Algorithms (EA) encompass a family of
robust search algorithms loosely inspired by natural
evolution. These algorithms are particularly useful to
solve problems for which classical algorithms of
optimisation, learning, or automatic design cannot
produce good results. In this thesis, we propose a
common methodological approach for the development of
EA-based intelligent systems. This methodological
approach is based on five principles: 1) to use
algorithms and representations that are problem
specific; 2) to develop hybrids between EA and
heuristics from the application field; 3) to take
advantage of multi-objective evolutionary optimization;
4) to do co-evolution for the simultaneous resolution
of several sub-problems of a common application and for
promoting robustness; and 5) to use generic software
tools for rapid development of unconventional EA. This
methodological approach is illustrated on four
applications of EA to hard problems. Moreover, the
fifth principle is explained in the study on genericity
of EA software tools.
The application of EA to complex problems requires the
use of generic software tool, for which we propose six
genericity criteria. Many EA software tools are
available in the community, but only a few are really
generic. Indeed, an evaluation of some popular tools
tells us that only three respect all these criteria, of
which the framework Open BEAGLE, developed during the
Ph.D. Open BEAGLE is organised into three main software
layers. The basic layer is made of the object oriented
foundations, over which there is the generic framework
layer, consisting of the general mechanisms of the
tool, and then the final layer, containing several
specialised frameworks implementing different EA
flavours. The tool also includes two extensions,
respectively to distribute the computations over many
computers and to visualise results.
Three applications illustrate different approaches for
using EA in the context of pattern recognition. First,
nearest neighbour classifiers are optimised, with the
prototype selection using a genetic algorithm
simultaneously to the Genetic Programming (GP) of
neighbourhood metrics. We add to this cooperative two
species co-evolution a third co-evolving competitive
species for selecting test data in order to improve the
generalisation capability of solutions. A second
application consists in designing representations with
GP for handwritten character recognition. This
evolutionary engineering is conducted with an automatic
positioning of regions in a window of attention,
combined with the selection of fuzzy sets for feature
extraction. This application is used to automate
character representation search, which is usually
conducted by human experts with a trial and error
process. For the third application in pattern
recognition, we propose an extensible system for the
hierarchical combination of classifiers into a fuzzy
decision tree. In this system, the tree topology is
evolved with GP while the numerical parameters of
classification units are determined by specialized
learning techniques. The system is tested with three
simple types of classification units. All of these
applications in pattern recognition have been
implemented using a two-objective fitness measure in
order to minimise classification errors and solutions
complexity. The last application demonstrate the
efficiency of EA for lens system design.
Self-adaptative evolution strategies, hybridised with a
specialised local optimisation technique, are used to
solve two complex optical design problems. In both
cases, the experiments demonstrate that hybridized EA
are able to produce results that are comparable or
better than those obtained by human experts. These
results are encouraging from the standpoint of a fully
automated optical design process. An additional
experiment is also conducted with a two-objectives
fitness measure that tries to maximise image quality
while minimising lens system cost.",
abstract = "Les algorithmes {\'e}volutionnaires (AE) constituent
une famille d{'}algorithmes inspir{\'e}s de
l{'}{\'e}volution naturelle. Ces algorithmes sont
particuli{\`e}rement utiles pour la r{\'e}solution de
probl{\`e}mes o{\`u} les algorithmes classiques
d{'}optimisation, d{'}apprentissage ou de conception
automatique sont incapables de produire des
r{\'e}sultats satisfaisants. On propose dans cette
th{\`e}se une approche m{\'e}thodologique pour le
d{\'e}veloppement de syst{\`e}mes intelligents
bas{\'e}s sur les AE. Cette approche m{\'e}thodologique
repose sur cinq principes : 1) utiliser des algorithmes
et des repr{\'e}sentations adapt{\'e}s au probl{\`e}me
; 2) d{\'e}velopper des hybrides entre des AE et des
heuristiques du domaine d{'}application ; 3) tirer
profit de l{'}optimisation {\'e}volutionnaire {\`a}
plusieurs objectifs ; 4) faire de la co-{\'e}volution
pour r{\'e}soudre simultan{\'e}ment plusieurs
sous-probl{\`e}mes d{'}une application ou favoriser la
robustesse ; et 5) utiliser un outil logiciel
g{\'e}n{\'e}rique pour le d{\'e}veloppement rapide
d{'}AE non conventionnels. Cette approche
m{\'e}thodologique est illustr{\'e}e par quatre
applications des AE {\`a} des probl{\`e}mes difficiles.
De plus, le cinqui{\`e}me principe est appuy{\'e} par
l{'}{\'e}tude sur la g{\'e}n{\'e}ricit{\'e} dans les
outils logiciels d{'}AE. Le d{\'e}veloppement
d{'}applications complexes avec les AE exige
l{'}utilisation d{'}un outil logiciel
g{\'e}n{\'e}rique. Six crit{\`e}res sont propos{\'e}s
ici pour {\'e}valuer la g{\'e}n{\'e}ricit{\'e} des
outils d{'}AE. De nombreux outils logiciels d{'}AE sont
disponibles dans la communaut{\'e}, mais peu d{'}entre
eux peuvent {\^e}tre v{\'e}ritablement qualifi{\'e}s de
g{\'e}n{\'e}riques. En effet, une {\'e}valuation de
quelques outils relativement populaires nous indique
que seulement trois satisfont pleinement {\`a} tous ces
crit{\`e}res, dont la framework d{'}AE Open BEAGLE,
d{\'e}velopp{\'e}e durant le doctorat.",
abstract = "Open BEAGLE est organis{\'e} en trois couches
logicielles principales, avec {\`a} la base les
fondations orient{\'e}es objet, sur lesquelles
s{'}ajoute une framework g{\'e}n {\'e}rique comprenant
les m{\'e}canismes g{\'e}n{\'e}raux de l{'}outil, ainsi
que plusieurs frameworks sp{\'e}cialis{\'e}es qui
implantent diff{\'e}rentes saveurs d{'}AE. L{'}outil
BibTeX entry too long. Truncated
Genetic Programming entries for
Christian Gagne