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 genetic programming-based model for predicting phosphorus concentration in shallow lakes
    Bai, Yu
    Yang, Jianquan
    Sun, Guojin
    Zhao, Yufeng
    Yu, Yu
    WATER PRACTICE AND TECHNOLOGY, 2022, 17 (03) : 637 - 644
  • [42] A genetic programming-based optimal sensor placement for greenhouse monitoring and control
    Ajani, Oladayo S.
    Aboyeji, Esther
    Mallipeddi, Rammohan
    Uyeh, Daniel Dooyum
    Ha, Yushin
    Park, Tusan
    FRONTIERS IN PLANT SCIENCE, 2023, 14
  • [43] A genetic programming-based convolutional neural network for image quality evaluations
    Kit Yan Chan
    Hak-Keung Lam
    Huimin Jiang
    Neural Computing and Applications, 2022, 34 : 15409 - 15427
  • [44] A genetic programming-based approach for classifying pancreatic adenocarcinoma: the SICED experience
    Gianni D’Angelo
    Maria Nunzia Scoppettuolo
    Anna Lisa Cammarota
    Alessandra Rosati
    Francesco Palmieri
    Soft Computing, 2022, 26 : 10063 - 10074
  • [45] Genetic Programming-based induction of a glucose-dynamics model for telemedicine
    De Falco, Ivanoe
    Della Cioppa, Antonio
    Koutny, Tomas
    Krcma, Michal
    Scafuri, Umberto
    Tarantin, Ernesto
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2018, 119 : 1 - 13
  • [46] A Constraint Programming-based Genetic Algorithm (CPGA) for Capacity Output Optimization
    Goh, Kate Ean Nee
    Chin, Jeng Feng
    Loh, Wei Ping
    Tan, Melissa Chea-Ling
    JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM, 2014, 7 (05): : 1222 - 1249
  • [47] A Multiobjective Genetic Programming-Based Ensemble for Simultaneous Feature Selection and Classification
    Nag, Kaustuv
    Pal, Nikhil R.
    IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (02) : 499 - 510
  • [48] Genetic programming-based self-reconfiguration planning for metamorphic robot
    Ababsa T.
    Djedl N.
    Duthen Y.
    International Journal of Automation and Computing, 2018, 15 (04) : 431 - 442
  • [49] Genetic programming-based discovery of ranking functions for effective Web search
    Fan, WG
    Gordon, MD
    Pathak, P
    JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 2005, 21 (04) : 37 - 56
  • [50] Genetic Programming-Based Empirical Model for Daily Reference Evapotranspiration Estimation
    Guven, Aytac
    Aytek, Ali
    Yuce, Mehmet Ishak
    Aksoy, Hafzullah
    CLEAN-SOIL AIR WATER, 2008, 36 (10-11) : 905 - 912