scholarly journals Using Formal Grammars as Musical Genome

2021 ◽  
Vol 11 (9) ◽  
pp. 4151
Author(s):  
David D. Albarracín-Molina ◽  
Alfredo Raglio ◽  
Francisco Rivas-Ruiz ◽  
Francisco J. Vico
Keyword(s):  
Genetic Algorithm ◽  
Real World ◽  
Fitness Function ◽  
Tonal Music ◽  
Formal Grammars ◽  
Generative Music ◽  
Engineering Problems ◽  
Representation Method ◽  
Basic Features ◽  
Program Interface

In this paper, we explore a generative music method that can compose atonal and tonal music in different styles. One of the main differences between regular engineering problems and artistic expressions is that goals and constraints are usually ill-defined in the latter case; in fact the rules here could or should be transgressed more regularly. For this reason, our approach does not use a pre-existing dataset to imitate or extract rules from. Instead, it uses formal grammars as a representation method than can retain just the basic features, common to any form of music (e.g., the appearance of rhythmic patterns, the evolution of tone or dynamics during the composition, etc.). Exploring different musical spaces is the responsibility of a program interface that translates musical specifications into the fitness function of a genetic algorithm. This function guides the evolution of those basic features enabling the emergence of novel content. In this study, we then assess the outcome of a particular music specification (guitar ballad) in a controlled real-world setup. As a result, the generated music can be considered similar to human-composed music from a perceptual perspective. This endorses our approach to tackle arts algorithmically, as it is able to produce novel content that complies with human expectations.

scholarly journals Application of Representation and Fitness Method of Genetic Algorithm for Class Scheduling System

2019 ◽  
Vol 8 (2) ◽  
pp. 1816-1821
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
The Other ◽  
State University ◽  
Scheduling System ◽  
Future Studies ◽  
Class Scheduling ◽  
Representation Method ◽  
Stated Problem ◽  
Poor Management

This paper introduced aimed to solve the poor management of schedule, one of the major problems at Isabela State University-Main Campus. Scheduling is a process conducted before a certain event would be executed. The study used and adopt the Representation and Fitness Methods of Genetic Algorithm to formulate a solution. The study showed that the adaptation of the two methods is well fitted for use in solving the stated problem. The representation method creates and generates the pre-scheduling template to be used for the plotting of schedules, and fitness method is how the pre-scheduling template generated and created. The researchers used some criterion of ISO 9126 Standard as an instrument to determine its functionality and usability. Results showed that the representation and fitness methods of the genetic algorithm make the scheduling process more accurate and reliable schedules, lessen the time-consumed and lessen the time-conflicts in the plotted schedules. For future studies to be conducted reformulation of fitness function to include the other components and variables of scheduling like individual schedules for both regular and irregular student and campus extension integration and considering the other indicator of the instrument used are significantly suggested.

scholarly journals Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

1995 ◽  
Vol 2 ◽  
pp. 369-409 ◽  
Author(s):  
P. D. Turney
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Real World ◽  
Average Cost ◽  
Fitness Function ◽  
Empirical Evaluation ◽  
The Third ◽  
Decision Tree Induction ◽  
Cost Sensitive Classification ◽  
Better Than

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals New Challenges Arising in Engineering Problems with Fractional and Integer Order

2021 ◽  
Vol 5 (2) ◽  
pp. 35
Author(s):  
Haci Mehmet Baskonus ◽  
Luis Manuel Sánchez Ruiz ◽  
Armando Ciancio
Keyword(s):  
Mathematical Models ◽  
Real World ◽  
Integer Order ◽  
Engineering Problems ◽  
New Challenges ◽  
Real World Problems

Mathematical models have been frequently studied in recent decades in order to obtain the deeper properties of real-world problems [...]

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

scholarly journals Advanced SIMION Techniques: Boundary Matching and Genetic Optimization

2015 ◽  
Vol 21 (S4) ◽  
pp. 218-223 ◽  
Author(s):  
D. Dowsett
Keyword(s):  
Genetic Algorithm ◽  
Real World ◽  
Great Accuracy ◽  
Genetic Optimization ◽  
Real World Applications ◽  
Boundary Matching

AbstractTwo techniques for use with SIMION [1] are presented, boundary matching and genetic optimization. The first allows systems which were previously difficult or impossible to simulate in SIMION to be simulated with great accuracy. The second allows any system to be rapidly and robustly optimized using a parallelized genetic algorithm. Each method will be described along with examples of real world applications.

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