http://hdl.handle.net/10016/3855 2013-05-22T04:33:00Z http://hdl.handle.net/10016/16988 Title: Multi-objective metaheuristics for multi-disciplinary engineering applications Author(s): Luna, Francisco; Isasi, Pedro Description: Editorial. Special Issue: Multi-objective metaheuristics for multi-disciplinary engineering applications 2012-01-31T23:00:00Z http://hdl.handle.net/10016/16040 Title: Optimizing the DFCN Broadcast Protocol with a Parallel Cooperative Strategy of Multi-Objective Evolutionary Algorithms Author(s): Segura, Carlos; Cervantes, Alejandro; Nebro, Antonio J.; Jaraíz-Simón, María Dolores; Segredo, Eduardo; García-Rodríguez, Sandra; Luna, Francisco; Gómez-Pulido, Juan A.; Miranda, Gara; Luque, Cristóbal; Alba, Enrique; Vega-Rodríguez, Miguel A.; León, Coromoto; Galván, Inés M. Abstract: This work presents the application of a parallel coopera- tive optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation im- plies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet sce- nario. The cooperation of a team of multi-objective evolutionary al- gorithms has been performed with a novel optimization model. Such model is a hybrid parallel algorithm that combines a parallel island- based scheme with a hyperheuristic approach. Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational resources to the most suitable algorithms. The obtained results for a manets scenario, representing a mall, demon- strate the validity of the new proposed approach. Description: Proceeding of: 5th International Conference, EMO 2009, Nantes, France, April 7-10, 2009 2008-12-31T23:00:00Z http://hdl.handle.net/10016/15883 Title: A lazy learning approach for building classification models Author(s): Galván, Inés M.; Valls, José M.; García, Miguel; Isasi, Pedro Abstract: In this paper, we propose a lazy learning strategy for building classification learning models. Instead of learning the models with the whole training data set before observing the new instance, a selection of patterns is made depending on the new query received and a classification model is learnt with those selected patterns. The selection of patterns is not homogeneous, in the sense that the number of selected patterns depends on the position of the query instance in the input space. That selection is made using a weighting function to give more importance to the training patterns that are more similar to the query instance. Our intention is to provide a lazy learning mechanism suited to any machine learning classification algorithm. For this reason, we study two different methods to avoid fixing any parameter. Experimental results show that classification rates of traditional machine learning algorithms based on trees, rules, or functions can be improved when they are learnt with the lazy learning approach proposed. 2011-04-30T22:00:00Z http://hdl.handle.net/10016/15869 Title: The Clock Proxy Auction for Allocating Radio Spectrum Licenses Author(s): Mochón, Asunción; Sáez, Yago; Gómez-Barroso, J.L.; Isasi, Pedro Abstract: The combinatorial clock-proxy auction is analyzed as a selling mechanism of a portion of the “digital dividend” in an European country. We assumed bidders with bounded rationality making their bidding decisions based on a system of recommendation that learns from the environment. The auction outcome when all bidders follow the proposed strategies was compared with the efficient outcome of the auction. Although significant differences were found in the seller’s income, no significant variations were found in the distribution of spectrum licenses among bidders. 2011-03-31T22:00:00Z