scholarly journals An genetic algorithm approach for profiling computational performance measures

2015 ◽  
Author(s):  
Matheus Sant Ana Lima
Keyword(s):  
Genetic Algorithm ◽  
Performance Measures ◽  
Fitness Function ◽  
Initial Population ◽  
Roulette Wheel Selection ◽  
Computational Performance ◽  
Genetic Algorithm Approach ◽  
Clustering Data ◽  
The Individual ◽  
Elitist Selection

This paper present a Genetic Algorithm(GA) approach for clustering data metric of computational performance measures collected from vmstat and sar tools. The proposed work models the genes, chromosomes, species and environment based on the dataset and presents an algorithm to analyze patterns and classify the records. The proposed method submits the performance information to an N-Dimensional Histogram in order to obtain the distribution of data that is used as input to the cluster initialization. The individual from each species undergoes successive crossover, mutation and selection operations to improve and evolve the initial population to a given environment state. The fitness-function is determined by the N-Dimensional Euclidean distance. The selection method is based on the Roulette-Wheel Selection, Elitist Selection and Truncation Selection. The results presented were obtained from seven test scenarios.

scholarly journals An genetic algorithm approach for profiling computational performance measures

2015 ◽  
Author(s):  
Matheus Sant Ana Lima
Keyword(s):  
Genetic Algorithm ◽  
Performance Measures ◽  
Fitness Function ◽  
Initial Population ◽  
Roulette Wheel Selection ◽  
Computational Performance ◽  
Genetic Algorithm Approach ◽  
Clustering Data ◽  
The Individual ◽  
Elitist Selection

This paper present a Genetic Algorithm(GA) approach for clustering data metric of computational performance measures collected from vmstat and sar tools. The proposed work models the genes, chromosomes, species and environment based on the dataset and presents an algorithm to analyze patterns and classify the records. The proposed method submits the performance information to an N-Dimensional Histogram in order to obtain the distribution of data that is used as input to the cluster initialization. The individual from each species undergoes successive crossover, mutation and selection operations to improve and evolve the initial population to a given environment state. The fitness-function is determined by the N-Dimensional Euclidean distance. The selection method is based on the Roulette-Wheel Selection, Elitist Selection and Truncation Selection. The results presented were obtained from seven test scenarios.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

scholarly journals Application of the genetic algorithm to the task of compiling the curriculum

2018 ◽  
Author(s):  
V. A. Turchina ◽  
D. O. Tanasienko
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Necessary Conditions ◽  
Fitness Function ◽  
Educational Process ◽  
Heuristic Methods ◽  
Educational Groups ◽  
The Individual ◽  
Negative Property

One of the main tasks in organizing the educational process in higher education is the drawing up of a schedule of classes. It reflects the weekly student and faculty load. At the same time, when compiling, there are a number of necessary conditions and a number of desirable. The paper considers seven required and four desirable conditions. In this paper, one of the well-known approaches that can be used in drawing up a curriculum is consid-ered. The proposed scheme of the genetic algorithm, the result of which is to obtain an approximate solution to the problem of scheduling with the need to further improve it by other heuristic methods. To solve the problem, an island model of the genetic algorithm was selected and its advantages were considered. In the paper, the author's own structure of the individual, which includes chromosomes in the form of educational groups and genes as a lesson at a certain time, is presented and justified. The author presents his own implementations of the genetic algorithms. During the work, many variants of operators were tested, but they were rejected due to their inefficiency. The biggest problem was to maintain the consistency of information encoded in chromosomes. Also, two post-steps were added: to try to reduce the number of teacher conflict conflicts and to normalize the schedule - to remove windows from the schedule. The fitness function is calculated according to the following principles: if some desired or desired property is present in the individual, then a certain number is deducted from the individual's assessment, if there is a negative property, then a certain number is added to the assessment. Each criterion has its weight, so the size of the fine or rewards may be different. In this work, fines were charged for non-fulfillment of mandatory conditions, and rewards for fulfilling the desired

scholarly journals Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

2020 ◽  
Author(s):  
Jiawei LI ◽  
Tad Gonsalves
Keyword(s):  
Genetic Algorithm ◽  
Real Data ◽  
Initial Population ◽  
Exam Timetabling ◽  
Timetabling Problem ◽  
Training Approach ◽  
Chromosome Representation ◽  
Genetic Algorithm Approach ◽  
Hard And Soft Constraints ◽  
Microsoft Office

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

scholarly journals Genetic Algorithm for Scheduling of Laboratory Personnel

2001 ◽  
Vol 47 (1) ◽  
pp. 118-123 ◽  
Author(s):  
James C Boyd ◽  
John Savory
Keyword(s):  
Genetic Algorithm ◽  
Medical Center ◽  
Fitness Function ◽  
Visual Basic ◽  
Work Environments ◽  
Skilled Workers ◽  
Laboratory Personnel ◽  
Work Shift ◽  
Work Tasks ◽  
Genetic Algorithm Approach

Abstract Background: Staffing core laboratories with appropriate skilled workers requires a process to schedule these individuals so that all workstations are appropriately filled and all the skills of each worker are exercised periodically to maintain competence. Methods: We applied a genetic algorithm to scheduling laboratory personnel. Our program, developed in Visual Basic 4.0, maximizes the value of a fitness function that measures how well a given scheduling of individuals and their skills matches a set of work tasks for a given work shift. The user provides in an Excel spreadsheet the work tasks, individuals available to work on any given date, and skills each individual possesses. The user also specifies the work shift to be scheduled, the range of dates to be scheduled, the number of days that an individual stays on a given workstation before rotating, and various parameters for the genetic algorithm if they differ from the default values. Results: For >22 months, the program matched individuals to those tasks for which they were qualified and maintained personnel skills by rotating job duties. The schedules generated by the program allowed supervisory personnel to anticipate dates far in advance of when worker availability would be limited, so staffing could be adjusted. In addition, the program helped to identify skills for which too few individuals had been trained. This program has been well accepted by the staff in the clinical laboratories of a 670-bed university medical center, saving 37 h of labor per month, or approximately $11 000 per year, in time that supervisory personnel have spent developing work schedules. Conclusions: The genetic algorithm approach appears to be useful for scheduling in highly technical work environments that employ multiskilled workers.

Optimal Sensor Placement Based on MAC and SPGA Algorithms

2012 ◽  
Vol 594-597 ◽  
pp. 1118-1122 ◽  
Author(s):  
Yong Ming Fu ◽  
Ling Yu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Optimization Method ◽  
Frame Structure ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Modal Assurance Criterion ◽  
Sensor Optimization ◽  
Computation Efficiency ◽  
The Individual

In order to solve the problem on sensor optimization placement in the structural health monitoring (SHM) field, a new sensor optimization method is proposed based on the modal assurance criterion (MAC) and the single parenthood genetic algorithm (SPGA). First, the required sensor numbers are obtained by using the step accumulating method. The SPGA is used to place sensors, in which the binary coding is adopted to realize the genetic manipulation through gene exchange, gene shift and gene inversion. Then, the method is further simplified and improved for higher computation efficiency. Where, neither the individual diversity of initial population nor the immature convergence problem is required. Finally, a numerical example of 61 truss frame structure is used to assess the robustness of the proposed method. The illustrated results show that the new method is better than the improved genetic algorithm and the step accumulating method in the search capacity, computational efficiency and reliability.

scholarly journals A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Selim ◽  
Hatem Khater
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Fitness Function ◽  
Support Vector ◽  
Initial Population ◽  
Tree Classifier ◽  
Selection Approach ◽  
Feature Selection Approach

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

scholarly journals Quality Enhancement of AC Chopper Using Genetic Algorithm

2015 ◽  
Vol 16 (1) ◽  
pp. 30
Author(s):  
S. Mahendran ◽  
Gnanambal I ◽  
Maheswari A
Keyword(s):  
Genetic Algorithm ◽  
Output Voltage ◽  
Fitness Function ◽  
Harmonic Distortion ◽  
Simulink Model ◽  
Harmonic Elimination ◽  
Global Optima ◽  
Genetic Algorithm Approach ◽  
Newton Raphson ◽  
Raphson Method

This paper mainly deals with the design of AC chopper using Genetic Algorithm based harmonic elimination technique. Genetic Algorithm is used to calculate optimum switching angles to eliminate lower order harmonics in the output voltage. Total Harmonic Distortion of output voltage is calculated from the obtained switching angles and also adopted in the proposed fitness function. Comparative analysis is made for the switching angles obtained by the Newton Raphson method and the proposed Genetic Algorithm. The analysis reveals that the proposed technique is on par with conventional method. Additionally, the Genetic Algorithm approach offers less computational burden, guaranteed global optima in most cases and faster convergence. The proposed method is simulated in Matlab/Simulink model and the results shows that the proposed method works with high effectiveness, accuracy and rapidity.

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