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Métaheuristiques adaptatives d'optimisation continue basées sur des méthodes d'apprentissage

Abstract : The problems of continuous optimization are numerous, in economics, in signal processing, in neural networks, and so on. One of the best-known and most widely used solutions is the evolutionary algorithm, a metaheuristic algorithm based on evolutionary theories that borrows stochastic mechanisms and has shown good performance in solving problems of continuous optimization. The use of this family of algorithms is very popular, despite the many difficulties that can be encountered in their design. Indeed, these algorithms have several parameters to adjust and a lot of operators to set according to the problems to solve. In the literature, we find a plethora of operators described, and it becomes complicated for the user to know which one to select in order to have the best possible result. In this context, this thesis has the main objective to propose methods to solve the problems raised without deteriorating the performance of these algorithms. Thus we propose two algorithms:- a method based on the maximum a posteriori that uses diversity probabilities for the operators to apply, and which puts this choice regularly in play,- a method based on a dynamic graph of operators representing the probabilities of transitions between operators, and relying on a model of the objective function built by a neural network to regularly update these probabilities. These two methods are detailed, as well as analyzed via a continuous optimization benchmark
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Submitted on : Monday, April 1, 2019 - 1:47:07 AM
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Asmaa Ghoumari. Métaheuristiques adaptatives d'optimisation continue basées sur des méthodes d'apprentissage. Traitement du signal et de l'image [eess.SP]. Université Paris-Est, 2018. Français. ⟨NNT : 2018PESC1114⟩. ⟨tel-02085935⟩



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