Algorithmes évolutionnaires appliqués à la reconnaissance des formes et à la conception optique

Authors: Gagné, Christian
Advisor: Parizeau, Marc
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 optimization, 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 organized 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 specialized frameworks implementing different EA flavors. The tool also includes two extensions, respectively to distribute the computations over many computers and to visualize results. Three applications illustrate different approaches for using EA in the context of pattern recognition. First, nearest neighbor classifiers are optimized, with the prototype selection using a genetic algorithm simultaneously to the Genetic Programming (GP) of neighborhood metrics. We add to this cooperative two species co-evolution a third coevolving competitive species for selecting test data in order to improve the generalization 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 minimize classification errors and solutions complexity. The last application demonstrate the efficiency of EA for lens system design. Selfadaptative evolution strategies, hybridized with a specialized 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 maximize image quality while minimizing lens system cost.
Document Type: Thèse de doctorat
Issue Date: 2005
Open Access Date: 11 April 2018
Grantor: Université Laval
Collection:Thèses et mémoires

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