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    Automatic parameter tuning in aluminum extrusion based on genetic programming
    (2020-04-01)
    Hanskunatai, Anantapom
    This work applies artificial intelligence in the aluminum extrusion process for automatic setting the ram speed of a machine according to the requirements of the industry. The automatic parameter tuning system computes the ram speed with the equation created by genetic programming (GP). In model evaluation, MAE and MAPE are used to measure a predictive performance of the models. In addition to GP, linear and polynomial regression are used to generate the automatic parameter tuning model for comparing a performance with GP. The experimental results on the test set show that GP performs the best in predictive performance with 0.130 of MAE and 4.22% of MAPE . Finally, the GP model has been developed as a software to calculate the ram speed and display it on a screen. This system will help users who are not proficient in aluminum extrusion or new users to have better control of production.
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    Multi-objective genetic programming with redundancy-regulations for automatic construction of image feature extractors
    (2010-01-01)
    Watchareeruetai, Ukrit
    ;
    Matsumoto, Tetsuya
    ;
    Takeuchi, Yoshinori
    ;
    Kudo, Hiroaki
    ;
    Ohnishi, Noboru
    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-TT. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-TT and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.