Genetic Programming-Based Code Generation for Arduino

被引:0
|
作者
Ferrel W. [1 ]
Alfaro L. [2 ]
机构
[1] Departamento Académico de Ingeniería Electrónica, Universidad Nacional de San Agustín de Arequipa Arequipa
[2] Departamento Académico de Ingeniería de Sistemas, Universidad Nacional de San Agustín de Arequipa Arequipa
来源
| 1600年 / Science and Information Organization卷 / 11期
关键词
Arduino based thermometer; Arduino mega board; automatic generation of programs; cooperative coevolutionary algorithm; Genetic programming; multi-objective linear genetic programming;
D O I
10.14569/IJACSA.2020.0111168
中图分类号
学科分类号
摘要
This article describes a methodology for writing the program for the Arduino board using an automatic generator of assembly language routines that works based on a cooperative coevolutionary multi-objective linear genetic programming algorithm. The methodology is described in an illustrative example that consists of the development of the program for a digital thermometer organized on a circuit formed by the Arduino Mega board, a text LCD module, and a temperature sensor. The automatic generation of a routine starts with an input-output table that can be created in a spreadsheet. The following routines have been automatically generated: initialization routine for the text LCD screen, routine for determining the temperature value, routine for converting natural binary code into unpacked two-digit BCD code, routine for displaying a symbol on the LCD screen. The application of this methodology requires basic knowledge of the assembly programming language for writing the main program and some initial configuration routines. With the application of this methodology in the illustrative example, 27% of the program lines were written manually, while the remaining 73% were generated automatically. The program, produced with the application of this methodology, preserves the advantage of assembly language programs of generating machine code much smaller than that generated by using the Arduino programming language. © 2020. All Rights Reserved.
引用
收藏
页码:538 / 549
页数:11
相关论文
共 50 条
  • [41] A comparative study of optimization models in genetic programming-based rule extraction problems
    Marconi de Arruda Pereira
    Eduardo Gontijo Carrano
    Clodoveu Augusto Davis Júnior
    João Antônio de Vasconcelos
    Soft Computing, 2019, 23 : 1179 - 1197
  • [42] Genetic programming-based pseudorandom number generator for wireless identification and sensing platform
    Kosemen, Cem
    Dalkilic, Gokhan
    Aydin, Omer
    TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2018, 26 (05) : 2500 - 2511
  • [43] Scale- and Rotation-Robust Genetic Programming-Based Corner Detectors
    Seo, Kisung
    Kim, Youngkyun
    APPLICATIONS OF EVOLUTIONARY COMPUTATION, PT I, PROCEEDINGS, 2010, 6024 : 381 - 391
  • [44] Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks
    Krzysztof Krawiec
    Genetic Programming and Evolvable Machines, 2002, 3 (4) : 329 - 343
  • [45] A Genetic Programming-based Wrapper Imputation Method for Symbolic Regression with Incomplete Data
    Al-Helali, Baligh
    Chen, Qi
    Xue, Bing
    Zhang, Mengjie
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 2395 - 2402
  • [46] Genetic programming-based fusion of HOG and LBP features for fully automated texture classification
    Hazgui, Mohamed
    Ghazouani, Haythem
    Barhoumi, Walid
    VISUAL COMPUTER, 2022, 38 (02) : 457 - 476
  • [47] A genetic programming-based model for drag coefficient of emergent vegetation in open channel flows
    Liu, Meng-Yang
    Huai, Wen-Xin
    Yang, Zhong-Hua
    Zeng, Yu-Hong
    ADVANCES IN WATER RESOURCES, 2020, 140
  • [48] Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers
    Chan, K. Y.
    Kwong, C. K.
    Fogarty, T. C.
    INFORMATION SCIENCES, 2010, 180 (04) : 506 - 518
  • [49] Genetic programming-based fusion of HOG and LBP features for fully automated texture classification
    Mohamed Hazgui
    Haythem Ghazouani
    Walid Barhoumi
    The Visual Computer, 2022, 38 : 457 - 476
  • [50] Forecasting Dendrolimus sibiricus Outbreaks: Data Analysis and Genetic Programming-Based Predictive Modeling
    Malashin, Ivan
    Masich, Igor
    Tynchenko, Vadim
    Nelyub, Vladimir
    Borodulin, Aleksei
    Gantimurov, Andrei
    Shkaberina, Guzel
    Rezova, Natalya
    FORESTS, 2024, 15 (05):