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

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

@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

Citations