Titlebook: Computational Intelligence; International Joint Juan Julian Merelo,Agostinho Rosa,Joaquim Filipe Conference proceedings 2016 Springer Inte

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A Statistical Approach to Dealing with Noisy Fitness in Evolutionary Algorithms. In this paper we propose new methods based on incremental computation (memory-based) or fitness average or, additionally, using statistical tests to impose a partial order on the population; this partial order is considered to assign a fitness value to every individual which can be used straightfo

招待

A Dissimilarity Learning Approach by Evolutionary Computation for Faults Recognition in Smart Gridsach of a multiple weighted Dissimilarity Measure, designed to cope with mixed data types like numerical data, Time Series and categorical data, and a One-Class Classification technique. For Categorical data the Semantic Distance (SD) is proposed, capable of grasping semantical information from clust

丛林

Noise Sensitivity of an Information Granules Filtering Procedure by Genetic Optimization for Inexact in the same sequence excerpt. In this paper, we present a method to overcome this drawback by applying an optimization-based step lter that identifies the most descriptive patterns among those found by the clustering process, and allows to return more compact and easily interpretable clusters. We e

CUR

Static and Dynamic Methods for Fuzzy Signal Processing of Sound and Electromagnetic Environment Basely the specific signal by removing background noise based on the fuzzy observation data in the sound environment under the existence of background noise. The effectiveness of the theoretically proposed static and dynamic signal processing methods is experimentally confirmed by applying those to real

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Timo Koski,Åbo Akademi,Wilfried Logesd with a reflexive transitive .-fuzzy relation .. In case the relation . is also symmetric, these measures coincide and we call their value by the measure of roughness of rough approximation. Basic properties of such measures are studied. A realization of measures of rough approximation in terms of .-fuzzy topologies is presented.