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Speeding-Up Evolutionary Algorithms to Solve Black-Box Optimization Problems

Echevarrieta, J.; Arza, E.; Pérez, A. (2024-01-10)

Population-based evolutionary algorithms are often considered when approaching computationally expensive black-box optimization problems. They employ a selection mechanism to choose the best solutions from a given population...

Fast Computation of Cluster Validity Measures for Bregman Divergences and Benefits

Capó, M.; Pérez, A.; Lozano, J.A. (2023-01-01)

Partitional clustering is one of the most relevant unsupervised learning and pattern recognition techniques. Unfortunately, one of the main drawbacks of these methodologies refer to the fact that the number of clusters is ge...

Fast K-Medoids With the l_1-Norm

Capó, M.; Pérez, A.; Lozano, J.A. (2023-07-26)

K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of the...

Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees

Álvarez, V.; Mazuelas, S.; Lozano, J.A. (2023-12-01)

For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity. The incremental learning of a growing sequence of tasks...