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This talk presents some recent advances in model-based evolutionary multi-objective optimization. We first present a regularity based estimation of distribution algorithm that uses a probabilistic model containing a principal curve and a local Gaussian model. We show that the proposed algorithm is able to work efficiently for large dimensional optimization problems with a small population size. Then,...
Artificial intelligence and games go way back. At least to Turing, who re-invented the Minimax algorithm to play Chess even before he had a computer, and to Samuel, who invented a predecessor of TD-learning in order to build a Checkers-playing program in the 1950s. Games are important for AI because they are designed to challenge and train human cognitive capabilities, and are thus uniquely relevant...
The present paper introduces M-fuzzy semi-quantales, fuzzifying semi-quantales, and (L, M)-quasi-fuzzy topological spaces, providing a common framework for (L, M)-fuzzy topological spaces of Kubiak and Šostak, L-quasi-fuzzy topological spaces of Rodabaugh and L-fuzzy topological spaces of Höhle and Šostak. In this paper, we set up a dual adjunction between the category of (L, M)-quasi-fuzzy topological...
The paper considers fuzzy rule based system for multi criteria group decision making problem. A novel version of TOPSIS method using interval type 2 fuzzy rule based system approach is proposed with the objective of improving the type 2 TOPSIS ability to deal with ambiguity through the combination of the mathematical process involved in the type 2 TOPSIS with the expert empirical knowledge. On the...
In this paper, two ways for automatically designing a hierarchical classifier is checked. This study deals with a specific context where is necessary to work with a few number of training samples (and often unbalanced), to manage the subjectivity of the different output classes and to take into account an imprecision degree in the input data. The aim is also to create an interpretable classification...
Here the potential use of artificial neural networks for the purpose of understanding the biological processes behind perception is investigated. Current work in computer vision is surveyed focusing on methods to determine how a neural network utilizes it's resources. Analogies between feature detectors in deep neural networks and signaling pathways in the human brain are made. With these analogies...
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