BBO Comparison with other Nature Inspired Algorithms to Resolve Mixels

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

@Article{Mittal:2013:IJARCET,
  author =       "Mittu Mittal and Gagandeep Kaur",
  title =        "{BBO} Comparison with other Nature Inspired Algorithms
                 to Resolve Mixels",
  journal =      "International Journal of Advanced Research in Computer
                 Engineering \& Technology",
  year =         "2013",
  volume =       "2",
  number =       "6",
  pages =        "2114--2118",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, GP, ACO, BBO,
                 DE, migration, mutation, PSO, remote sensing",
  ISSN =         "2278-1323",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:cab44e3afa090ffe3a62aec0c44566cd",
  URL =          "http://ijarcet.org/wp-content/uploads/VOLUME-2-ISSUE-6-2114-2118.pdf",
  size =         "5 pages",
  abstract =     "Remote sensing is defined as a technique for acquiring
                 the information about an object without making physical
                 contact with that image via remote sensors. But the
                 major problem of remotely sensed images is mixed pixel
                 which always degrades the image quality. In this paper
                 we attempted to present an approach for resolving the
                 mixed pixels by using optimisation/ Evolutionary
                 algorithm i.e. Bio-geography based optimisation. EAs
                 are the most well known algorithms among nature
                 inspired algorithms, which is based on the biological
                 evolution in nature that is being responsible for the
                 design of all living beings on earth. A family of
                 successful EAs comprises genetic algorithm (GA),
                 genetic programming (GP), Differential Evolution,
                 evolutionary strategy (ES) , Artificial Bee Colony
                 Algorithm (ABC), Particle swarm optimisation (PSO), Ant
                 Colony Optimisation (ACO). This paper also deals with
                 the comparison of BBO and others EAs so that we can
                 proof BBO as best algorithm for resolving MIXELS
                 problem.",
  notes =        "Shri Pannalal Research Institute of Technology",
}

Genetic Programming entries for Mittu Mittal Gagandeep Kaur

Citations