Uncertainty analysis of an evolutionary algorithm to develop remote sensing spectral indices

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Momm:2008:SPIE,

• author = "Henrique G Momm and Greg Easson and Joel Kuszmaul",
• title = "Uncertainty analysis of an evolutionary algorithm to develop remote sensing spectral indices",
• booktitle = "Image Processing: Algorithms and Systems VI",
• year = "2008",
• editor = "Jaakko T. Astola and Karen O. Egiazarian and Edward R. Dougherty",
• volume = "6812",
• pages = "68120A.1--68120A.9",
• address = "San Jose, California, USA",
• month = "28 " # jan,
• publisher = "SPIE--The International Society for Optical Engineering",
• keywords = "genetic algorithms, genetic programming",
• DOI = "DOI:10.1117/12.766367",
• abstract = "The need for information extracted from remotely sensed data has increased in recent decades. To address this issue, research is being conducted to develop a complete multi-stage supervised object recognition system. The first stage of this system couples genetic programming with standard unsupervised clustering algorithms to search for the optimal preprocessing function. This manuscript addresses the quantification and the characterisation of the uncertainty involved in the random creation of the first set of candidate solutions from which the algorithm begins. We used a Monte Carlo type simulation involving 800 independent realisations and then analyzed the distribution of the final results. Two independent convergence approaches were investigated: [1] convergence based solely on genetic operations (standard) and [2] convergence based on genetic operations with subsequent insertion of new genetic material (restarting). Results indicate that the introduction of new genetic material should be incorporated into the preprocessing framework to enhance convergence and to reduce variability.",

}

Genetic Programming entries for Henrique G Momm Greg Easson Joel S Kuszmaul

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