scholarly journals A Novel Genetic Algorithm approach for Network Design with Robust Fitness Function

2010 ◽  
pp. 459-465 ◽  
Author(s):  
Anand Kumar ◽  
N. N. Jani
Keyword(s):  
Genetic Algorithm ◽  
Network Design ◽  
Fitness Function ◽  
Genetic Algorithm Approach

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 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.

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.

Tuning AutoNowcaster Automatically

2012 ◽  
Vol 27 (6) ◽  
pp. 1568-1579 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
John Crockett ◽  
Kenneth Sperow ◽  
Mamoudou Ba ◽  
Lingyan Xin
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Automated System ◽  
Forecast Verification ◽  
National Weather Service ◽  
Genetic Algorithm Approach ◽  
Tuning Process ◽  
Automated Tuning ◽  
Weather Service

Abstract AutoNowcaster (ANC) is an automated system that nowcasts thunderstorms, including thunderstorm initiation. However, its parameters have to be tuned to regional environments, a process that is time consuming, labor intensive, and quite subjective. When the National Weather Service decided to explore using ANC in forecast operations, a faster, less labor-intensive, and objective mechanism to tune the parameters for all the forecast offices was sought. In this paper, a genetic algorithm approach to tuning ANC is described. The process consisted of choosing datasets, employing an objective forecast verification technique, and devising a fitness function. ANC was modified to create nowcasts offline using weights iteratively generated by the genetic algorithm. The weights were generated by probabilistically combining weights with good fitness, leading to better and better weights as the tuning process proceeded.The nowcasts created by ANC using the automatically determined weights are compared with the nowcasts created by ANC using weights that were the result of manual tuning. It is shown that nowcasts created using the automatically tuned weights are as skilled as the ones created through manual tuning. In addition, automated tuning can be done in a fraction of the time that it takes experts to analyze the data and tune the weights.

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.

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