Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach

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@Article{Garg:2015:JCPa,
  author =       "Akhil1 Garg and Jasmine Siu Lee Lam",
  title =        "Improving environmental sustainability by formulation
                 of generalized power consumption models using an
                 ensemble based multi-gene genetic programming
                 approach",
  journal =      "Journal of Cleaner Production",
  volume =       "102",
  pages =        "246--263",
  year =         "2015",
  ISSN =         "0959-6526",
  DOI =          "doi:10.1016/j.jclepro.2015.04.068",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0959652615004436",
  abstract =     "Environmental sustainability is an important aspect
                 for accessing the performance of any machining
                 industry. Growing demand of customers for better
                 product quality has resulted in an increase in energy
                 consumption and thus a lower environmental performance.
                 Optimization of both product quality and energy
                 consumption is needed for improving economic and
                 environmental performance of the machining operations.
                 However, for achieving the global multi-objective
                 optimization, the models formulated must be able to
                 generalize the data accurately. In this context, an
                 evolutionary approach of multi-gene genetic programming
                 (MGGP) can be used to formulate the models for product
                 quality (surface roughness and tool life) and power
                 consumption. MGGP develops the model structure and its
                 coefficients based on the principles of genetic
                 algorithm (GA). Despite being widely applied, MGGP
                 generates models that may not give satisfactory
                 performance on the test data. The main reason behind
                 this is the inappropriate formulation procedure of the
                 multi-gene model and the difficulty in model selection.
                 Therefore, the present work proposes a new
                 ensemble-based-MGGP (EN-MGGP) framework that makes use
                 of statistical and classification strategies for
                 improving the generalization ability. The EN-MGGP
                 approach is applied on the reliable experimental
                 database (outputs: surface roughness, tool life and
                 power consumption) obtained from the literature, and
                 its performance is compared to that of the standardized
                 MGGP. The proposed EN-MGGP models outperformed the
                 standardized MGGP models. The conducted sensitivity and
                 parametric analysis validates the robustness of the
                 models by unveiling the non-linear relationships
                 between the outputs (surface roughness, tool life and
                 power consumption) and input parameters. It was also
                 found that the cutting speed has the most significant
                 impact on the power consumption in turning of AISI 1045
                 steel and the turning of 7075 Al alloy- 15 wtpercent
                 SIC composites. The generalized EN-MGGP models obtained
                 can easily be optimized analytically for attaining the
                 optimum input parameter settings that optimize the
                 product quality and power consumption simultaneously.",
  keywords =     "genetic algorithms, genetic programming, Environmental
                 sustainability, Power consumption, Product quality,
                 Machining, Surface roughness",
}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam

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