BBO Comparison with other Nature Inspired Algorithms to Resolve Mixels

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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