Multi-sensor fusion: an Evolutionary algorithm approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Maslov:2005:IF,
  author =       "Igor V. Maslov and Izidor Gertner",
  title =        "Multi-sensor fusion: an Evolutionary algorithm
                 approach",
  journal =      "Information Fusion",
  year =         "2006",
  volume =       "7",
  pages =        "304--330",
  number =       "3",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6W76-4FBM1CY-2/2/e57f81dddd02342a16c54961518cedde",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Information
                 fusion, Global optimization, Heuristic methods,
                 Evolutionary algorithms, Evolution strategies,
                 Evolutionary programming",
  DOI =          "doi:10.1016/j.inffus.2005.01.001",
  abstract =     "Modern decision-making processes rely on data coming
                 from different sources. Intelligent integration and
                 fusion of information from distributed multi-source,
                 multi-sensor network requires an optimisation-centred
                 approach. Traditional optimization techniques often
                 fail to meet the demands and challenges of highly
                 dynamic and volatile information flow. New methods are
                 required, which are capable of fully automated
                 adjustment and self-adaptation to fluctuating inputs
                 and tasks. One such method is Evolutionary algorithms
                 (EA), a generic, flexible, and versatile framework for
                 solving complex problems of global optimisation and
                 search in real world applications. The evolutionary
                 approach provides a valuable alternative to traditional
                 methods used in information fusion, due to its inherent
                 parallel nature and its ability to deal with difficult
                 problems. However, the application of the algorithm to
                 a particular problem is often more an art than science.
                 Choosing the right model and parameters requires an
                 in-depth understanding of the morphological development
                 of the algorithm, as well as its recent advances and
                 trends. This paper attempts to give a compact overview
                 of both basic and advanced concepts, models, and
                 variants of Evolutionary algorithms in various
                 implementations and applications particularly those in
                 information fusion. We have brought together material
                 scattered throughout numerous books, journal papers,
                 and conference proceedings. Strong emphasis is made on
                 the practical aspects of the EA implementation,
                 including specific and detailed recommendations drawn
                 from these various sources. However, the practical
                 aspects are discussed from the standpoint of concepts
                 and models, rather than from applications in specific
                 problem domains, which emphasise the generality of the
                 provided recommendations across different applications
                 including information fusion.",
}

Genetic Programming entries for Igor V Maslov Izidor Gertner

Citations