Automatic generation method of twelve tone row for musical composition used genetic algorithm

2009 ◽  
Author(s):  
Yoichiro Maeda ◽  
Yusuke Kajihara
Keyword(s):  
Genetic Algorithm ◽  
Automatic Generation ◽  
Musical Composition ◽  
Twelve Tone

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

scholarly journals Automatic Generation of Regular Expressions for Extracting Attribute Values of Medical Products

2018 ◽  
Vol 56 (1) ◽  
pp. 193-204
Author(s):  
Tomasz Łukaszuk ◽  
Mariusz Ferenc
Keyword(s):  
Genetic Algorithm ◽  
Automatic Generation ◽  
Medical Services ◽  
Regular Expressions ◽  
Huge Number ◽  
Medical Products ◽  
Product Specification

Abstract Resources of professional companies operating on the medical services market contain data from a huge number of transactional documents. This allows them to collect and process, among other actions, information about medical products. Organized data is obviously more valuable. In this paper, the possibility of supporting the process of organizing information is considered, with the goal to extract values of attributes of medical products from brief descriptions in transactional documents. This helps to build a structured product specification and makes it possible to make comparisons between products. For this purpose, an approach based on regular expressions and their generation with the use of the genetic algorithm is proposed. The results presented in the paper show a great potential of the presented method.

scholarly journals Automatic Generation and Evolution of Personalized Curriculum Based on Genetic Algorithm

2019 ◽  
Vol 14 (12) ◽  
pp. 15
Author(s):  
Xiaocong Duan
Keyword(s):  
Genetic Algorithm ◽  
Personalized Learning ◽  
Automatic Generation ◽  
User Model ◽  
Evolution System ◽  
Framework Structure ◽  
System Framework ◽  
Learning Efficiency ◽  
Learning Content ◽  
Generation Technology

In order to solve the automatic generation and evolution of personalized curriculum, the method of using genetic algorithm to realize the evolution of personalized learning content is proposed to solve the dynamic personalized needs of users. Through the research and implementation of personalized curriculum generation technology, firstly, the structures of curriculum generation, genetic algorithm and curriculum scene, as well as the method and technology of personalized curriculum generation and evolution system framework are described in detail. Then, the framework structure based on genetic algorithm is determined, and the user model is updated. Finally, experiments are carried out based on genetic algorithm. The research on the experiment of automatic generation and evolution of personalized curriculum shows that the application of genetic algorithm in the process of curriculum generation and evolution makes students' learning content evolve with the change of their knowledge state in the process of learning, effectively promotes students' interest in learning, and improves learning efficiency and effect.

scholarly journals Automatic Generation of Nurse Scheduling Table Using Genetic Algorithm

2002 ◽  
Vol 122 (6) ◽  
pp. 1023-1032 ◽  
Author(s):  
Hiroharu Kawanaka ◽  
Kosuke Yamamoto ◽  
Tomohiro Yoshikawa ◽  
Tsuyoshi Shinogi ◽  
Shinji Tsuruoka
Keyword(s):  
Genetic Algorithm ◽  
Automatic Generation ◽  
Nurse Scheduling

Automatic Generation of Musical Tone Row and Rhythm Based on the Twelve-Tone Technique Using Genetic Algorithm

2010 ◽  
Vol 14 (3) ◽  
pp. 288-296
Author(s):  
Yoichiro Maeda ◽  
◽  
Yusuke Kajihara
Keyword(s):  
Genetic Algorithms ◽  
Music Composition ◽  
Fitness Function ◽  
Automatic Generation ◽  
Computer Applications ◽  
Twelve Tone ◽  
Musical Rhythm ◽  
Interactive Genetic Algorithms ◽  
Musical Tone ◽  
The Relationship

Genetic Algorithms (GA) and Interactive Genetic Algorithms (IGA) used to generate sound in computer applications generating music are difficult to use, as is, in directly composing music. We propose music composition based on the 12-Tone Technique (TTT). In TTT composition, the melody and rhythm are usually created separately. The melody is created first to determine the musical subject and atmosphere. We design a fitness function based on the relationship between consonant and dissonant intervals that are a part of general musical theory and generate the 12-Tone (TT) row automatically by searching for consonant tone rows using the GA. We then set a fitness function for evaluating the rhythm we define, and obtained musical rhythm using the GA.

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