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.

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.

Multi-Attribution Classification Based on Lower Integral with Genetic Algorithm

2014 ◽  
Vol 668-669 ◽  
pp. 1090-1093
Author(s):  
Ai Xia Chen ◽  
Jun Hua Li
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Linear Programming Problem ◽  
Fuzzy Integral ◽  
Data Sets ◽  
Fuzzy Measures ◽  
Benchmark Data ◽  
Minimum Potential ◽  
And Performance ◽  
Set Function

Fuzzy integral has been widely used in multi-attribution classification when the interactions exist between the attributions. Because the fuzzy measure defined on the attributions represents the weights of all the attributions and the interactions between them. The lower integral is a type of fuzzy integral with respect to fuzzy measures, which represents the minimum potential of efficiency for a group of attributions with interaction. The value of lower integrals can be evaluated through solving a linear programming problem. Considering the lower integral as a classifier, this paper investigates its implementation and performance. The difficult step in the implementation is how to learn the non-additive set function used in lower integrals. And Genetic algorithm is used to solve the problem. Finally, numerical simulations on some benchmark data sets are given.

scholarly journals Completely Automated CNN Architecture Design Based on Blocks

2020 ◽  
Author(s):  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
GG Yen
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Classification Performance ◽  
Data Sets ◽  
Computational Resource ◽  
Problem Domain ◽  
Benchmark Data ◽  
Interested User

© 2019 IEEE. The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.

scholarly journals Completely Automated CNN Architecture Design Based on Blocks

2020 ◽  
Author(s):  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
GG Yen
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Classification Performance ◽  
Data Sets ◽  
Computational Resource ◽  
Problem Domain ◽  
Benchmark Data ◽  
Interested User

© 2019 IEEE. The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.

scholarly journals Multi-criteria calibration of a conceptual runoff model using a genetic algorithm

2000 ◽  
Vol 4 (2) ◽  
pp. 215-224 ◽  
Author(s):  
J. Seibert
Keyword(s):  
Genetic Algorithm ◽  
Synthetic Data ◽  
Test Case ◽  
True Parameter ◽  
Groundwater Levels ◽  
Optimisation Algorithm ◽  
Level Data ◽  
Parameter Values ◽  
Hbv Model ◽  
Runoff Model

Abstract. Abstract: Calibration of a model against more than one output variable is important for reliable simulations of internal processes. In this study, a genetic algorithm combined with local optimisation was proposed for automatic single- and multi-criteria calibration of the HBV model, a conceptual runoff model. The model and the optimisation algorithm were applied in two catchments with different geology where, in addition to observed runoff, time series of groundwater level data were available. For a theoretical, error-free test case with synthetic data, the optimisation algorithm was usually able to find the true parameter values. For the real-world case, parameter values varied considerably when calibrating against runoff only. However, parameter values were constrained significantly when calibrating against both runoff and groundwater levels. Furthermore, for one of the catchments, the results of the multi-criteria calibration motivated a modification of the model structure. Keywords: Multi-criteria calibration; genetic algorithm; parameter uncertainty; conceptual runoff models; HBV model; groundwater levels

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.

Genetic Algorithm Inversion of the 1997 Geoacoustic Inversion Workshop Test Case Data

1998 ◽  
Vol 06 (01n02) ◽  
pp. 61-71 ◽  
Author(s):  
Garry J. Heard ◽  
David Hannay ◽  
Scott Carr
Keyword(s):  
Genetic Algorithm ◽  
Transmission Loss ◽  
Propagation Model ◽  
Test Case ◽  
Test Cases ◽  
Data Sets ◽  
Vertical Array ◽  
Geoacoustic Inversion ◽  
Data Set ◽  
Case Data

An analysis of the 1997 Geoacoustic Inversion Workshop test case data was carried out to benchmark the performance of a Genetic Algorithm (GA) inversion code called SAGA_INV.1 The inversion program made use of Westwood's ORCA propagation model,2 FORTRAN subroutines, and Interactive Data Language (Research Systems Inc. IDL). SAGA_INV is capable of performing inversions with either Simulated Annealing (SA) or GA optimization schemes; however, only the GA portion of the code has been benchmarked with the workshop test cases at the present time. Not all of the workshop test cases were processed: this study was concerned only with the CAL, SD, SO, AT, and WA data sets. The CAL data was processed using three different cost functions: (i) standard Bartlett processor, (ii) a broadband coherent processor, and (iii) a transmission loss mismatch function. These processors were applied to three frequency bands: (i) 76 frequencies between 25 Hz and 100 Hz, (ii) nine frequencies between 28 Hz and 36 Hz, and (iii) 13 frequencies between 44 Hz and 56 Hz. The latter two frequency regimes were intended to simulate 1/3-octave bands centered at 32 Hz and 50 Hz, respectively. Four different receiving arrays were simulated: (i) a 1550 m aperture horizontal, bottom mounted array at approximately 1-km range, (ii) a similar array at approximately 4.2-km range, (iii) a 55-m aperture 12-element vertical array located at 1-km range, and (iv) a similar vertical array at 5-km range. In addition to processing the CAL data set, all three subcases of the SD, SO, AT, and WA data sets were also processed; however, only the transmission loss cost function and the two simulated 1/3-octave bands were considered for these test cases.

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.

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