Algorithmes evolutionnaires appliques a la reconnaissance des formes et a la conception optique

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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
                 comporte {\'e}galement deux extensions servant {\`a}
                 distribuer des calculs sur plusieurs ordinateurs et
                 {\`a} visualiser des r{\'e}sultats. Ensuite, trois
                 applications illustrent diff{\'e}rentes approches
                 d{'}utilisation des AE dans un contexte de
                 reconnaissance des formes. Premi{\`e}rement, on
                 optimise des classifieurs bas{\'e}s sur la r{\`e}gle du
                 plus proche voisin avec la s{\'e}lection de prototypes
                 par un algorithme g{\'e}n{\'e}tique, simultan{\'e}ment
                 {\`a} la construction de mesures de voisinage par
                 programmation g{\'e}n{\'e}tique (PG). {\`A} cette
                 co-{\'e}volution coop{\'e}rative {\`a} deux
                 esp{\`e}ces, on ajoute la co-{\'e}volution
                 comp{\'e}titive d{'}une troisi{\`e}me esp{\`e}ce pour
BibTeX entry too long. Truncated

Genetic Programming entries for Christian Gagne

Citations