Javier Apolloni

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This paper presents a new distributed differential evolution (dDE) algorithm and evaluates it according to the standard procedure set in the special session of continuous optimization of CECpsila05. We statistically validate our results in continuous optimization versus several other efficient techniques. Our distributed differential evolution is simple and(More)
In this work we evaluate a Particle Swarm Optimizer hybridized with Differential Evolution and apply it to the Black-Box Optimization Benchmarking for noisy functions (BBOB 2009). We have performed the complete procedure established in this special session dealing with noisy functions with dimension: 2, 3, 5, 10, 20, and 40 variables. Our proposal obtained(More)
In this work we evaluate a Particle Swarm Optimizer hybridized with Differential Evolution and apply it to the Black-Box Optimization Benchmarking for noiseless functions (BBOB 2009). We have performed the complete procedure established in this special session dealing with noiseless functions with dimension: 2, 3, 5, 10, 20, and 40 variables. Our proposal(More)
Los algoritmos de optimización basados en Cúmulos de Partículas (Particle Swarm Optimization-PSO) [1] y Evolución Diferencial (Differential Evolution-DE) [2] sup i ] (1 ≤ i ≤ D). Porúltimo, x inf i , x sup i ∈ R corresponden a los límites inferior (inf) y superior (sup) del dominio de la variable, respectivamente. En este trabajo estamos interesados en(More)
—The efficient selection of predictive and accurate gene subsets for cell-type classification is nowadays a crucial problem in Microarray data analysis. The application and combination of dedicated computational intelligence methods holds a great promise for tackling the feature selection and classification. In this work we present a Differential Evolution(More)
This paper presents a new distributed Differential Evolution (dDE) algorithm and provides an exhaustive evaluation of it by using two standard benchmarks. One of them was proposed in the special session of Real-Parameter Optimization of CEC'05, and the other was proposed in the special session of Large Scale Global Optimization of CEC'08. We statistically(More)
One of the most challenging aspects of the control theory is the design and implementation of controllers that can deal with changing environments, i. e., non stationary systems. Quite good progress has been made on this area by using different kind of models: neural networks, fuzzy systems, evolutionary algorithms, etc. Our approach consists in the use of(More)