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
  • [31] 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
  • [32] Genetic programming-based symbolic regression for goal-oriented dimension reduction
    Dorgo, Gyula
    Kulcsar, Tibor
    Abonyi, Janos
    CHEMICAL ENGINEERING SCIENCE, 2021, 244
  • [33] On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain
    Almarimi, Nuri
    Ouni, Ali
    Chouchen, Moataz
    Saidani, Islem
    Mkaouer, Mohamed Wiem
    2020 ACM/IEEE 15TH INTERNATIONAL CONFERENCE ON GLOBAL SOFTWARE ENGINEERING, ICGSE, 2020, : 43 - 54
  • [34] Multitask Genetic Programming-Based Generative Hyperheuristics: A Case Study in Dynamic Scheduling
    Zhang, Fangfang
    Mei, Yi
    Su Nguyen
    Tan, Kay Chen
    Zhang, Mengjie
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (10) : 10515 - 10528
  • [35] Genetic Programming-Based Selection of Imputation Methods in Symbolic Regression with Missing Values
    Al-Helali, Baligh
    Chen, Qi
    Xue, Bing
    Zhang, Mengjie
    AI 2020: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2020, 12576 : 163 - 175
  • [36] Evaluation of genetic programming-based models for simulating friction factor in alluvial channels
    Roushangar, Kiyoumars
    Mouaze, Dominique
    Shiri, Jalal
    JOURNAL OF HYDROLOGY, 2014, 517 : 1154 - 1161
  • [37] A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS
    Nikdel, Zahra
    Beigy, Hamid
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2012, 11 (02)
  • [38] Parallel performance modeling using a genetic programming-based error correction procedure
    Raghavachar, Kavitha
    Mahinthakumar, G. Kumar
    Worley, Patrick
    Zechman, Emily
    Ranjithan, Ranji
    SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, 2007, 83 (07): : 515 - 527
  • [39] Development of genetic programming-based model for predicting oyster norovirus outbreak risks
    Chenar, Shima Shamkhali
    Deng, Zhiqiang
    WATER RESEARCH, 2018, 128 : 20 - 37
  • [40] A comparative study of optimization models in genetic programming-based rule extraction problems
    Pereira, Marconide Arruda
    Carrano, Eduardo Gontijo
    Davis Junior, Clodoveu Augusto
    de Vasconcelos, Joao Antonio
    SOFT COMPUTING, 2019, 23 (04) : 1179 - 1197