Browsing by Keyword "Differential evolution"
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Item Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes(Springer Verlag, 2018) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Fister, Iztok; Yin, Hujun; Novais, Paulo; Camacho, David; Tallón-Ballesteros, Antonio J.; IA; QuantumAssociation rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.Item Iterative power and subcarrier allocation in rate-constrained orthogonal multi-carrier downlink systems based on hybrid harmony search heuristics(2011-08) Del Ser, Javier; Bilbao, Miren Nekane; Gil-López, Sergio; Matinmikko, Marja; Salcedo-Sanz, Sancho; IAThis paper presents a novel iterative hybrid algorithm for subcarrier and power allocation in a cognitive orthogonal frequency division multiple access (OFDMA) downlink. In the considered setup a primary base station forwards information to K distant receivers by using a single OFDM waveform, whereas a secondary base station subject to stringent per-user rate constraints interferes with the former by sending information from users to the same set of destinations. Power and user allocation at both base stations is jointly performed by the proposed algorithm to maximize the overall throughput of the setup while satisfying, at the same time, the imposed rate constraints. Our proposal, which stems from an hybridization of the harmony search (HS) and differential evolution (DE) algorithms along with a greedy local repair method, is shown through computer simulations over the extended vehicular A ITU channel model to be an effective and practical resource allocation procedure for cognitive OFDMA downlinks.Item Novelty search for global optimization(2019-04-15) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Fister, Iztok; Perc, Matjaž; Slavinec, Mitja; IA; QuantumNovelty search is a tool in evolutionary and swarm robotics for maintaining the diversity of population needed for continuous robotic operation. It enables nature-inspired algorithms to evaluate solutions on the basis of the distance to their k-nearest neighbors in the search space. Besides this, the fitness function represents an additional measure for evaluating the solution, with the purpose of preserving the so-named novelty solutions into the next generation. In this study, a differential evolution was hybridized with novelty search. The differential evolution is a well-known algorithm for global optimization, which is applied to improve the results obtained by the other solvers on the CEC-14 benchmark function suite. Furthermore, functions of different dimensions were taken into consideration, and the influence of the various novelty search parameters was analyzed. The results of experiments show a great potential for using novelty search in global optimization.Item Using adaptive novelty search in differential evolution(Springer Verlag, 2019) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Fister, Iztok; De La Prieta, Fernando; González-Briones, Alfonso; Pawleski, Pawel; Calvaresi, Davide; Del Val, Elena; Julian, Vicente; Lopes, Fernando; Osaba, Eneko; Sánchez-Iborra, Ramón; IA; QuantumNovelty search ensures evaluation of solutions in stochastic population-based nature-inspired algorithms according to additional measure, where each solution is evaluated by a distance to its neighborhood beside the fitness function. Thus, the population diversity is preserved that is a prerequisite for the open-ended evolution in evolutionary robotics. Recently, the Novelty search was applied for solving the global optimization into differential evolution, where all Novelty search parameters remain unchanged during the run. The novelty area width parameter, that determines the diameter specifying the minimum change in each direction needed the solution for treating as the novelty, has a crucial influence on the optimization results. In this study, this parameter was adapted during the evolutionary process. The proposed self-adaptive differential evolution using the adaptive Novelty search were applied for solving the CEC 2014 Benchmark function suite, and the obtained results confirmed the usefulness of the adaptation.Item Using novelty search in differential evolution(Springer Verlag, 2018) Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Corchado, Juan M.; Julian, Vicente; Osaba Icedo, Eneko; Bajo, Javier; Hoffa-Dabrowska, Patrycja; Silveira, Ricardo Azambuja; Fernandez, Alberto; Giroux, Sylvain; Navarro Martínez, Elena María; Mathieu, Philippe; Castro, Antonio J.; Sanchez-Pi, Nayat; del Val, Elena; Unland, Rainer; Fuentes-Fernandez, Ruben; IA; QuantumNovelty search in evolutionary robotics measures a distance of potential novelty solutions to their k-nearest neighbors in the search space. This distance presents an additional objective to the fitness function, with which each individual in population is evaluated. In this study, the novelty search was applied within the differential evolution. The preliminary results on CEC-14 Benchmark function suite show its potential for using also in the future.