scholarly journals Genetic Algorithm Optimization for Determining Fuzzy Measures from Fuzzy Data

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chen Li ◽  
Gong Zeng-tai ◽  
Duan Gang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real Data ◽  
Difficult Problem ◽  
Particle Swarm Algorithm ◽  
Fuzzy Data ◽  
Algorithm Optimization ◽  
Practical Applications ◽  
Fuzzy Measures ◽  
Choquet Integrals

Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is a very difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms, neural networks, and particle swarm algorithm, it is hard to say which one is more appropriate and more feasible. Each method has its advantages. Most of the existed works can only deal with the data consisting of classic numbers which may arise limitations in practical applications. It is not reasonable to assume that all data are real data before we elicit them from practical data. Sometimes, fuzzy data may exist, such as in pharmacological, financial and sociological applications. Thus, we make an attempt to determine a more generalized type of general fuzzy measures from fuzzy data by means of genetic algorithms and Choquet integrals. In this paper, we make the first effort to define theσ-λrules. Furthermore we define and characterize the Choquet integrals of interval-valued functions and fuzzy-number-valued functions based onσ-λrules. In addition, we design a special genetic algorithm to determine a type of general fuzzy measures from fuzzy data.

scholarly journals Maximization of Eigenfrequency Gaps in a Composite Cylindrical Shell Using Genetic Algorithms and Neural Networks

2019 ◽  
Vol 9 (13) ◽  
pp. 2754 ◽  
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Excitation Frequency ◽  
Algorithm Optimization ◽  
External Excitation ◽  
Excitation Frequencies ◽  
Objective Function Values ◽  
Using Data ◽  
Laminated Structures

This paper presents a novel method for the maximization of eigenfrequency gaps around external excitation frequencies by stacking sequence optimization in laminated structures. The proposed procedure enables the creation of an array of suggested lamination angles to avoid resonance for each excitation frequency within the considered range. The proposed optimization algorithm, which involves genetic algorithms, artificial neural networks, and iterative retraining of the networks using data obtained from tentative optimization loops, is accurate, robust, and significantly faster than typical genetic algorithm optimization in which the objective function values are calculated using the finite element method. The combined genetic algorithm–neural network procedure was successfully applied to problems related to the avoidance of vibration resonance, which is a major concern for every structure subjected to periodic external excitations. The presented examples illustrate a combined approach to avoiding resonance through the maximization of a frequency gap around external excitation frequencies complemented by the maximization of the fundamental natural frequency. The necessary changes in natural frequencies are caused only by appropriate changes in the lamination angles. The investigated structures are thin-walled, laminated one- or three-segment shells with different boundary conditions.

scholarly journals Implementation of Genetic Algorithms and Momentum Backpropagation in Classification of Subtype Cells Acute Myeloid Leukimia

2020 ◽  
Vol 14 (2) ◽  
pp. 189
Author(s):  
Dian Mustikaningrum ◽  
Retantyo Wardoyo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
White Blood Cells ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Learning Rate ◽  
Algorithm Optimization ◽  
Selection Parameter ◽  
Acute Myeloid ◽  
Random Trial

 Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation  is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution.The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study.The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

scholarly journals Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm

2016 ◽  
Author(s):  
Tarik Eltaeib ◽  
Julius Dichter
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Genetic Algorithm ◽  
Algorithm Optimization ◽  
Parallel Genetic Algorithms ◽  
Np Completeness ◽  
Linear Polynomial ◽  
Optimization Function ◽  
Speed Up ◽  
Complementary Equation

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

Application of Genetic Algorithms in the Engine Technology Selection Process

2004 ◽  
Vol 126 (4) ◽  
pp. 693-700 ◽  
Author(s):  
Bryce Roth ◽  
Chirag Patel
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Technology Selection ◽  
Algorithm Optimization ◽  
Identification Evaluation ◽  
Function Formulation ◽  
Visualization Techniques ◽  
Technology Identification ◽  
The Impact

The objective of this paper is to demonstrate the application of genetic algorithms to the engine technology selection process. The “technology identification, evaluation, and selection” method is discussed in conjunction with genetic algorithm optimization as a technique to quickly evaluate the impact of various technologies and select the subset with the highest potential payoff. Techniques used to model various aspects of engine technologies are described, with emphasis on technology constraints and their impact on the combinatorial optimization of technologies. Challenges include objective function formulation and development of models to deal with incompatibilities among different technologies. Typical results are presented for an 80-technology optimization using various visualization techniques to assist in easy interpretation of genetic algorithm results. Finally, several ideas for future development of these methods are briefly explored.

scholarly journals Optimization of Application of Genetic Algorithm Using C4.5 Method to Predict Breast Cancer Disease

bit-Tech ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Hartana Wijaya
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Techniques ◽  
The Body ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
C4.5 Algorithm ◽  
Parameter Values ◽  
Risk Of Cancer

Cancer is a big challenge for humanity. Cancer can affect various parts of the body. This deadly disease can be found in humans of all ages. However, the risk of cancer increases with age. Breast cancer is the most common cancer among women, and is the biggest cause of death for women. Then there are problems in the detection of breast cancer, causing patients to experience unnecessary treatment and huge costs. In a similar study, there were several methods used but there were problems due to the shape of nonlinear cancer cells. The C4.5 method can solve this problem, but C4.5 is weak in terms of determining parameter values, so it needs to be optimized. Genetic Algorithm is one of the good optimization methods, therefore the parameter values ​​of C4.5 will be optimized using Genetic Algorithms to get the best parameter values. The results of this study are that C4.5 Algorithm based on genetic algorithm optimization has a higher accuracy value (96%) than only using the C4.5 algorithm (94.99%) and which is optimized with the PSO algorithm (95.71%). This is evident from the increase in the value of accuracy of 1.01% for the C4.5 algorithm model that has been optimized with genetic algorithms. So it can be concluded that the application of genetic algorithm optimization techniques can increase the value of accuracy in the C4.5 algorithm.

scholarly journals A Novel Multiband Miniature Planar Inverted F Antenna Design for Bluetooth and WLAN Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
J. M. Jeevani W. Jayasinghe ◽  
Disala Uduwawala
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Ground Plane ◽  
Antenna Design ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Shorting Pin ◽  
5 Ghz ◽  
Patch Geometry ◽  
Compact Planar

A novel compact planar inverted F antenna (PIFA) optimized using genetic algorithms for 2.4 GHz (Bluetooth) and 5 GHz (UNII-1, UNII-2, UNII-2 extended, and UNII-3) bands is presented. The patch with a shorting pin is on a20×7×0.762 mm3substrate, which is suspended in air 5 mm above a30×7 mm2ground plane. Genetic algorithm optimization (GAO) is used to optimize the patch geometry, feed position, and shorting pin position simultaneously. Simulations are carried out by using HFSS and a prototype antenna is fabricated to compare the measurements with the simulations. The antenna shows fractional impedance bandwidths of 4% and 21% and gains of 2.5 dB and 3.2 dB at lower and upper bands, respectively.

scholarly journals StatBreak: Identifying “Lucky” Data Points Through Genetic Algorithms

2020 ◽  
Vol 3 (2) ◽  
pp. 216-228
Author(s):  
Hannes Rosenbusch ◽  
Leon P. Hilbert ◽  
Anthony M. Evans ◽  
Marcel Zeelenberg
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Statistical Analysis ◽  
Effect Size ◽  
Bayes Factor ◽  
Real Data ◽  
Model Fit ◽  
P Value ◽  
Data Points ◽  
Study Designs

Sometimes interesting statistical findings are produced by a small number of “lucky” data points within the tested sample. To address this issue, researchers and reviewers are encouraged to investigate outliers and influential data points. Here, we present StatBreak, an easy-to-apply method, based on a genetic algorithm, that identifies the observations that most strongly contributed to a finding (e.g., effect size, model fit, p value, Bayes factor). Within a given sample, StatBreak searches for the largest subsample in which a previously observed pattern is not present or is reduced below a specifiable threshold. Thus, it answers the following question: “Which (and how few) ‘lucky’ cases would need to be excluded from the sample for the data-based conclusion to change?” StatBreak consists of a simple R function and flags the luckiest data points for any form of statistical analysis. Here, we demonstrate the effectiveness of the method with simulated and real data across a range of study designs and analyses. Additionally, we describe StatBreak’s R function and explain how researchers and reviewers can apply the method to the data they are working with.

scholarly journals Kinematic arrangement optimization of a quadruped robot with genetic algorithms

2018 ◽  
Vol 51 (9-10) ◽  
pp. 406-416 ◽  
Author(s):  
Mehmet Mert Gülhan ◽  
Kemalettin Erbatur
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Performance Metrics ◽  
Dynamic Performance ◽  
Performance Measure ◽  
Kinematic Parameter ◽  
Algorithm Optimization ◽  
Performance Improvements ◽  
Performance Specifications ◽  
Dynamics Simulations

Background: As research on quadruped robots grows, so does the variety of designs available. These designs are often inspired by nature and finalized around various technical, instrumentation-based constraints. However, no systematic methodology of kinematic parameter selection to reach performance specifications is reported so far. Kinematic design optimization with objective functions derived from performance metrics in dynamic tasks is an underexplored, yet promising area. Methods: This article proposes to use genetic algorithms to handle the designing process. Given the dynamic tasks of jumping and trotting, body and leg link dimensions are optimized. The performance of a design in genetic algorithm search iterations is evaluated via full-dynamics simulations of the task. Results: The article presents comparisons of design results optimized for jumping and trotting separately. Significant dimensional dissimilarities and associated performance differences are observed in this comparison. A combined performance measure for jumping and trotting tasks is studied too. It is discussed how significantly various structural lengths affect dynamic performances in these tasks. Results are compared to a relatively more conventional quadruped design too. Conclusions: The task-specific nature of this optimization process improves the performances dramatically. This is a significant advantage of the systematic kinematic parameter optimization over straight mimicking of nature in quadruped designs. The performance improvements obtained by the genetic algorithm optimization with dynamic performance indices indicate that the proposed approach can find application area in the design process of a variety of robots with dynamic tasks.

Application of Genetic Algorithms in the Engine Technology Selection Process

2003 ◽  
Author(s):  
Bryce Roth ◽  
Chirag Patel
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Process ◽  
Technology Selection ◽  
Algorithm Optimization ◽  
Identification Evaluation ◽  
Function Formulation ◽  
Visualization Techniques ◽  
Technology Identification ◽  
The Impact

The objective of this paper is to demonstrate the application of Genetic Algorithms to the engine technology selection process. The “Technology Identification, Evaluation, and Selection” method is discussed in conjunction with Genetic Algorithm optimization as a technique to quickly evaluate the impact of various technologies and select the subset with the highest potential payoff. Techniques used to model various aspects of engine technologies are described, with emphasis on technology constraints and their impact on the combinatorial optimization of technologies. Challenges include objective function formulation and development of models to deal with incompatibilities among different technologies. Typical results are presented for an 80-technology optimization using various visualization techniques to assist in easy interpretation of Genetic Algorithm results. Finally, several ideas for future development of these methods are briefly explored.

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