AUTOMATIC GENERATION OF FUZZY MODELS BY GENETIC ALGORITHMS

2002 ◽  
Vol 35 (1) ◽  
pp. 385-390 ◽  
Author(s):  
Elaine Yassue Nagai ◽  
Lúcia Valéria Ramos de Arruda
Keyword(s):  
Genetic Algorithms ◽  
Automatic Generation ◽  
Fuzzy Models

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.

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

Automatic generation of interlock designs using genetic algorithms

2017 ◽  
Vol 101 ◽  
pp. 167-192 ◽  
Author(s):  
Yeremia Yehuda Lepar ◽  
Yu-Chih Wang ◽  
Chuei-Tin Chang
Keyword(s):  
Genetic Algorithms ◽  
Automatic Generation

Automatic generation of energy conservation measures in buildings using genetic algorithms

2011 ◽  
Vol 43 (10) ◽  
pp. 2718-2726 ◽  
Author(s):  
V. Siddharth ◽  
P.V. Ramakrishna ◽  
T. Geetha ◽  
Anand Sivasubramaniam
Keyword(s):  
Genetic Algorithms ◽  
Energy Conservation ◽  
Automatic Generation ◽  
Conservation Measures ◽  
Energy Conservation Measures

GENERATION OF OPTIMAL SEQUENCE OF MACHINING OPERATIONS IN SETUP PLANNING BY GENETIC ALGORITHMS

2012 ◽  
Vol 11 (01) ◽  
pp. 67-80 ◽  
Author(s):  
CHANDAN KUMAR ◽  
SANKHA DEB
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real Number ◽  
Automatic Generation ◽  
Precedence Constraints ◽  
Binary Digit ◽  
Setup Planning ◽  
Optimal Sequence ◽  
Machining Operations ◽  
Selection Of

This paper aims at automatic generation of optimal sequence of machining operations in setup planning by Genetic Algorithm (GA) based on minimizing the number of setup changes and tool changes, subject to various machining precedence constraints. The GA has been reconstructed as the method of representing an operation is not as simple as assigning it a binary digit as in case of a chromosome in traditional GA but it has to be a distinct real number. Accordingly, the GA operators had to be modified. At the end of each GA cycle, there might be chromosomes having high fitness values but not conforming to constraints. Moreover, due to randomness of GA, the conformable chromosomes might tend to get lost. In order to minimize such losses, the elitist model is used for selection of chromosomes. Furthermore, a special subroutine has been developed to check the chromosomes for conformability and modify/repair those that violate the constraints.

A Genetic Engineering Approach to Genetic Algorithms

2001 ◽  
Vol 9 (1) ◽  
pp. 71-92 ◽  
Author(s):  
John S. Gero ◽  
Vladimir Kazakov
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Engineering ◽  
Genetic Material ◽  
Evolutionary Process ◽  
Automatic Generation ◽  
Engineering Approach ◽  
Standard Genetic Algorithm

We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.

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