2006
DOI: 10.1016/j.inffus.2005.01.001
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Multi-sensor fusion: an Evolutionary algorithm approach

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Cited by 38 publications
(20 citation statements)
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“…2) Evolutionary Algorithms: Article [111] gives an overview of basic and advanced concepts, models, and variants of GA in various applications in information fusion. …”
Section: B Data Aggregation and Sensor Fusionmentioning
confidence: 99%
“…2) Evolutionary Algorithms: Article [111] gives an overview of basic and advanced concepts, models, and variants of GA in various applications in information fusion. …”
Section: B Data Aggregation and Sensor Fusionmentioning
confidence: 99%
“…1 We must mention that the word "fusion," which appears often in the aforementioned references, usually refers to the "combination" of classifiers for improving classifier performance using a single training data set and not necessarily to "data fusion" (combining information coming from different data sources). Commonly used methods for "data fusion" are generally based on Bayesian theory [32], [33], state estimation with Kalman or particle filtering [34]- [39], evidence theory (DS) [40], [41] and its variations [10], [42]- [45], information theoretic framework [46], neural networks [47], and evolutionary algorithms [48], [49]. The traditional application area for data fusion has long been target detection and tracking [38], [39], [50]- [54].…”
Section: B Ensemble Approaches and Data Fusionmentioning
confidence: 99%
“…EAs have been widely applied in the last few years in computationally expensive applications and proved to be a strong optimization method in many types of combinatorial problems. Those successful applications of EAs involve scheduling [6], knowledge discovery [7], information fusion [8], etc.…”
Section: Genetic Algorithmsmentioning
confidence: 99%