Use of evolutionary computation techniques for exploration and prediction of helicopter loads

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

@InProceedings{Cheung:2012:CEC,

• title = "Use of evolutionary computation techniques for exploration and prediction of helicopter loads",
• author = "Catherine Cheung and Julio J. Valdes and Matthew Li",
• pages = "1130--1137",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6252905",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Ensemble Methods in Computational Intelligence (IEEE-CEC), Defence and cyber security, Classification, clustering, data analysis and data mining",
• abstract = "The development of accurate load spectra for helicopters is necessary for life cycle management and life extension efforts. This paper explores continued efforts to use evolutionary computation (EC) methods and machine learning techniques to estimate several helicopter dynamic loads. Estimates for the main rotor normal bending (MRNBX) on the Australian Black Hawk helicopter were generated from an input set that included thirty standard flight state and control system parameters under several flight conditions (full speed forward level flight, rolling left pullout at 1.5g, and steady 45deg left turn at full speed). Multiobjective genetic algorithms (MOGA) used in combination with the Gamma test found reduced subsets of predictor variables with Madelin potential. These subsets were used to estimate MRNBX using Cartesian genetic programming and neural network models trained by deterministic and evolutionary computation techniques, including particle swarm optimization (PSO), differential evolution (DE), and MOGA. PSO and DE were used alone or in combination with deterministic methods. Different error measures were explored including a fuzzy-based asymmetric error function. EC techniques played an important role in both the exploratory and Madelin phase of the investigation. The results of this work show that the addition of EC techniques in the modelling stage generated more accurate and correlated models than could be obtained using only deterministic optimization.",
• notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Catherine Cheung Julio J Valdes Matthew Li

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