scholarly journals ALGORITMA GENETIK UNTUK OPTIMASI PARAMETER MODEL TANGKI PADA ANALISIS TRANSFORMASI DATA HUJAN-DEBIT

2012 ◽  
Vol 13 (1) ◽  
pp. 85 ◽  
Author(s):  
Sulianto Sulianto ◽  
Ernawan Setiono
Keyword(s):  
Genetic Algorithm ◽  
River Flow ◽  
Optimal Parameter ◽  
Optimization Process ◽  
Data Sets ◽  
Parallel Composition ◽  
Systems Of Equations ◽  
Optimal Model ◽  
Testing Data ◽  
Parameter Values

Fundamental weaknesses of the application of Tank Models is on so many parameters whose values should be set firstbefore the model is simultaneously applied. This condition causes the Tank Models is considered inefficient to solve practical problems. This study is an attempt to improve the performance of Tank Models can be applied to more practical and effective for the analysis of the data transformation of rainfall into river flow data. The discussion in this study focused on efforts to solve systems of equations Tank Models Series Composition, Parallel Composition and Combined Composition with the use of genetic algorithms in the optimization process parameters, so that the resulting system of equations to determine the optimal model parameter values are automatically in the studied watersheds. The results showed that the Wonorejo Watershed, Genetic Algorithm to solve the optimization process Tank Models parameter values as well. In the generation-150 showed the three models can achieve convergence with similar fitness values . Testing optimal parameter values by using the testing data sets show that the Tank Models Combined composition with Genetic Algorithm-based tend to be more consistent than the other two types of Tank Models.

Multi-Frequency Matched-Field Inversion of Benchmark Data Using a Genetic Algorithm

1998 ◽  
Vol 06 (01n02) ◽  
pp. 135-150 ◽  
Author(s):  
D. G. Simons ◽  
M. Snellen
Keyword(s):  
Genetic Algorithm ◽  
Test Cases ◽  
Data Sets ◽  
True Parameter ◽  
Benchmark Data ◽  
Line Array ◽  
Standard Normal ◽  
Water Test ◽  
Parameter Values ◽  
Field Inversion

For a selected number of shallow water test cases of the 1997 Geoacoustic Inversion Workshop we have applied Matched-Field Inversion to determine the geoacoustic and geometric (source location, water depth) parameters. A genetic algorithm has been applied for performing the optimization, whereas the replica fields have been calculated using a standard normal-mode model. The energy function to be optimized is based on the incoherent multi-frequency Bartlett processor. We have used the data sets provided at a few frequencies in the band 25–500 Hz for a vertical line array positioned at 5 km from the source. A comparison between the inverted and true parameter values is made.

Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

2014 ◽  
Vol 697 ◽  
pp. 239-243 ◽  
Author(s):  
Xiao Hui Liu ◽  
Yong Gang Xu ◽  
De Ying Guo ◽  
Fei Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Rolling Mill ◽  
Optimal Parameter ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Gear Box ◽  
Parameter Values

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.

MEMS Accelerometer Design Optimization Using Genetic Algorithm

2013 ◽  
Vol 705 ◽  
pp. 288-294 ◽  
Author(s):  
V.S. Krushnasamy ◽  
A. Vimala Juliet
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
High Efficiency ◽  
Microelectromechanical Systems ◽  
Optimal Parameter ◽  
Low Cost ◽  
Complex Nature ◽  
Mems Accelerometer ◽  
Mems Technology ◽  
Parameter Values

MEMS (Microelectromechanical Systems) refers to the technology integrating electrical and mechanical components with feature size of 1~1000 microns. Due to its small size, low cost, low power consumption and high efficiency, MEMS technology has been widely used in many fields.In this paper,the design optimization of MEMS accelerometer is discussed.The main objective of this investigation is to find a optimum design of MEMS,which satisfies a set of given constraints. The accelerometer employs a double folded beam flexure system and the mass being displaced is the proof mass.Due to the complex nature of the problem,a genetic algorithm (GA) is developed for the optimization of MEMS.The GA attempts to minimize the die area and so the four optimal parameter values can be determined. MEMS accelerometers can be used in air-bag deployment systems in automobiles.The experimental results will show the optimal design of MEMS.

scholarly journals A novel bottom-left packing genetic algorithm for analog module placement

2003 ◽  
Vol 1 ◽  
pp. 191-196 ◽  
Author(s):  
L. Zhang ◽  
U. Kleine
Keyword(s):  
Genetic Algorithm ◽  
Integrated Circuits ◽  
Cost Function ◽  
Bin Packing ◽  
Optimal Parameter ◽  
Packing Problem ◽  
Fractional Factorial ◽  
Genetic Encoding ◽  
The Cost ◽  
Parameter Values

Abstract. This paper presents a novel genetic algorithm for analog module placement. It is based on a generalization of the two-dimensional bin packing problem. The genetic encoding and operators assures that all constraints of the problem are always satisfied. Thus the potential problems of adding penalty terms to the cost function are eliminated, so that the search configuration space decreases drastically. The dedicated cost function covers the special requirements of analog integrated circuits. A fractional factorial experiment was conducted using an orthogonal array to study the algorithm parameters. A meta-GA was applied to determine the optimal parameter values. The algorithm has been tested with several local benchmark circuits. The experimental results show this promising algorithm makes the better performance than simulated annealing approach with the satisfactory results comparable to manual placement.

scholarly journals Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model

2011 ◽  
Vol 14 (3) ◽  
pp. 784-799 ◽  
Author(s):  
Wen-Chuan Wang ◽  
Chun-Tian Cheng ◽  
Kwok-Wing Chau ◽  
Dong-Mei Xu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Evolutionary Process ◽  
Model Parameters ◽  
Optimal Model ◽  
Xinanjiang Model ◽  
Chaos Genetic Algorithm ◽  
Parameter Values ◽  
Stochastic Properties ◽  
Search Capability

Conceptual rainfall–runoff (CRR) model calibration is a global optimization problem with the main objective to find a set of optimal model parameter values that attain a best fit between observed and simulated flow. In this paper, a novel hybrid genetic algorithm (GA), which combines chaos and simulated annealing (SA) method, is proposed to exploit their advantages in a collaborative manner. It takes advantage of the ergodic and stochastic properties of chaotic variables, the global search capability of GA and the local optimal search capability of SA method. First, the single criterion of the mode calibration is employed to compare the performance of the evolutionary process of iteration with GA and chaos genetic algorithm (CGA). Then, the novel method together with fuzzy optimal model (FOM) is investigated for solving the multi-objective Xinanjiang model parameters calibration. Thirty-six historical floods with one-hour routing period for 5 years (2000–2004) in Shuangpai reservoir are employed to calibrate the model parameters whilst 12 floods in two recent years (2005–2006) are utilized to verify these parameters. The performance of the presented algorithm is compared with GA and CGA. The results show that the proposed hybrid algorithm performs better than GA and CGA.

PARAMETERS OPTIMIZE METHOD BASED ON GENETIC AND SIMULATED ANNEALING ALGORITHMS

2008 ◽  
Vol 22 (01) ◽  
pp. 169-181 ◽  
Author(s):  
JIAO-MIN LIU ◽  
JING-HONG WANG
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Initial Study ◽  
Direct Selection ◽  
Data Sets ◽  
Theoretical Support ◽  
Fuzzy Extension Matrix ◽  
Annealing Algorithm ◽  
Parameter Values

This paper gives an initial study on the comparison between Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Firstly, a new algorithm is presented. This method combines Genetic Algorithm and Simulated Annealing Algorithm, and it can be used to optimize the three parameters α, β and γ. It involes the rules that are extracted from Fuzzy Extension Matrix (FEM). These parameters play an important part in the entire process of rule extraction based on FEM. Secondly, it provides some theoretical support to the direct selection of the parameter values through experiments. Lastly, five data sets from the UCI Machine Learning centers are employed in the study. Experimental results and discussions are given.

scholarly journals On the exponent in the Von Bertalanffy growth model

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4205 ◽  
Author(s):  
Katharina Renner-Martin ◽  
Norbert Brunner ◽  
Manfred Kühleitner ◽  
Werner Georg Nowak ◽  
Klaus Scheicher
Keyword(s):  
Optimal Parameter ◽  
Growth Function ◽  
Model Parameters ◽  
Data Sets ◽  
Scaling Exponent ◽  
Von Bertalanffy ◽  
Large Variability ◽  
Metabolic Scaling ◽  
Main Observation ◽  
Parameter Values

Von Bertalanffy proposed the differential equation m′(t) = p × m(t)a − q × m(t) for the description of the mass growth of animals as a function m(t) of time t. He suggested that the solution using the metabolic scaling exponent a = 2/3 (Von Bertalanffy growth function VBGF) would be universal for vertebrates. Several authors questioned universality, as for certain species other models would provide a better fit. This paper reconsiders this question. Based on 60 data sets from literature (37 about fish and 23 about non-fish species) it optimizes the model parameters, in particular the exponent 0 ≤ a < 1, so that the model curve achieves the best fit to the data. The main observation of the paper is the large variability in the exponent, which can vary over a very large range without affecting the fit to the data significantly, when the other parameters are also optimized. The paper explains this by differences in the data quality: variability is low for data from highly controlled experiments and high for natural data. Other deficiencies were biologically meaningless optimal parameter values or optimal parameter values attained on the boundary of the parameter region (indicating the possible need for a different model). Only 11 of the 60 data sets were free of such deficiencies and for them no universal exponent could be discerned.

PARAMETERS ESTIMATION OF DOUBLE EXPONENTIAL SMOOTHING FOR HAND JITTER REDUCTION USING GENETIC ALGORITHM

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Nor Farizan Zakaria ◽  
Mohd Asyraf Zulkifley ◽  
Mohd Marzuki Mustafa
Keyword(s):  
Genetic Algorithm ◽  
Optimal Parameter ◽  
Exponential Smoothing ◽  
Parameters Estimation ◽  
Prediction Algorithm ◽  
Optimal Parameter Estimation ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Fast Prediction ◽  
Parameter Values

Hand jitter is a natural tremor that has become a concern in many areas such as microsurgery, collaborative environment and tele-tutoring as it can cause imprecision, inaccuracy and misleading pointer information. Over the recent years, many researches have been done to reduce hand jitter but they are either too complex or too time consuming. Hence, to overcome the limitations, Double Exponential Smoothing (DES) has been used as an alternative, which is a simple and fast prediction algorithm. However, estimating the parameter values of DES is a difficult process that requires juggling between several criteria. In this paper, an optimal parameter estimation technique of DES using Genetic Algorithm was developed to find the optimal parameter values. Thorough comparisons have been made with previous methods to prove the magnitude of improvement. Our study found that the proposed method is able to reduce the hand jitter by 52% compared to the benchmarked methods. Hence, DES is suitable to be implemented in many applications that require precise and accurate hand-based pointing system. 

scholarly journals Membangun Sistem Informasi Penjadwalan Dengan Metode Algoritma Genetika Pada Laboratorium Teknik Informatika Universitas Muhammadiyah Maluku Utara

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Agil Assagaf ◽  
Adelina Ibrahim ◽  
Catur Suranto
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Parameter ◽  
Optimal Schedule ◽  
Optimization Method ◽  
Computational Approach ◽  
Biological Processes ◽  
Practice Schedule ◽  
Parameter Values ◽  
Number Of Individuals

Abstrak : Penjadwalan praktikum merupakan proses penyusunan jadwal pelaksanaan yang menginformasikan sejumlah mata kuliah, dosen yang mengajar, ruang, serta waktu kegiatan perkuliahan di laboratorium. Perlu diperhatikan beberapa aspek untuk menyusun jadwal perkuliahan yang sesuai dengan kebutuhan. Aspek yang perlu diperhatikan antara lain adalah aspek dari dosen yang mengajar, mata kuliah yang diajar. Penyusunan jadwal secara manual cenderung membutuhkan waktu yang lebih lama dan ketelitian yang cukup bagi pembuat jadwal. Untuk dapat membuat jadwal yang optional, dibutuhkan metode optimasi. Pada penelitian ini, akan diuji coba metode optimasi dalam pembuatan jadwal praktikum yaitu Algoritma Genetika. Algoritma genetika merupakan pendekatan komputasional untuk menyelesaikan masalah yang dimodelkan dengan proses biologi dari evolusi. Parameter-parameter Algoritma Genetika yang mempengaruhi jadwal perkuliahan yang dihasilkan adalah jumlah individu, probabilitas crossover, probabilitas mutasi serta metode seleksi, crossover yang digunakan. Pengujian dilakukan dengan cara mencari nilai parameter-parameter algoritma genetika yang paling optimal dalam jadwal perkuliahan. Hasil penelitian menunjukkan bahwa dengan jumlah generasi, jumlah individu, probabilitas crossover dan probabilitas mutasi dapat menghasilkan jadwal yang paling optimal.Kata kunci: Optimasi, Penjadwalan, Seleksi, Crossover, Mutasi, Algoritma GenetikaAbstract : Practical scheduling is the process of preparation of an implementation schedule that informs a number of courses, lecturers who teach, space, and time of lecture activities in the laboratory. It should be noted several aspects to arrange lecture schedule in accordance with the needs. Aspects that need to be considered include aspects of lecturers who teach, courses taught. Manual scheduling tends to take longer and enough accuracy for the schedule maker. To be able to create an optional schedule, an optimization method is required. In this research, will be tested the optimization method in the preparation of the practice schedule that is Genetic Algorithm. Genetic algorithms are a computational approach to solving problems modeled by biological processes of evolution. The parameters of the Genetic Algorithm affecting the course schedule are the number of individuals, the probability of crossover, the probability of mutation and the method of selection, the crossover used. Testing is done by finding the most optimal parameter values of genetic algorithm in lecture schedule. The results show that with the number of generations, the number of individuals, the probability of crossover and the probability of mutation can produce the most optimal schedule.  Keywords: Optimization, Scheduling, Selection, Crossover, Mutation, Genetic Algorithm

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