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.

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.

scholarly journals Optimalisasi Penyelesaian Knapsack Problem Dengan Algoritma Genetika

2016 ◽  
pp. 182
Author(s):  
I Wayan Supriana
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Everyday Life ◽  
Knapsack Problem ◽  
Selection Process ◽  
Optimization Algorithms ◽  
Evolutionary Process ◽  
Knapsack Problems ◽  
Theory Of Evolution ◽  
Selection Of

Knapsack problems is a problem that often we encounter in everyday life. Knapsack problem itself is a problem where a person faced with the problems of optimization on the selection of objects that can be inserted into the container which has limited space or capacity. Problems knapsack problem can be solved by various optimization algorithms, one of which uses a genetic algorithm. Genetic algorithms in solving problems mimicking the theory of evolution of living creatures. The components of the genetic algorithm is composed of a population consisting of a collection of individuals who are candidates for the solution of problems knapsack. The process of evolution goes dimulasi of the selection process, crossovers and mutations in each individual in order to obtain a new population. The evolutionary process will be repeated until it meets the criteria o f an optimum of the resulting solution. The problems highlighted in this research is how to resolve the problem by applying a genetic algorithm knapsack. The results obtained by the testing of the system is built, that the knapsack problem can optimize the placement of goods in containers or capacity available. Optimizing the knapsack problem can be maximized with the appropriate input parameters.

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.

Hangzhou Housing Demand Forecasting Model Based on BP Neural Network of Genetic Algorithm Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 37-41 ◽  
Author(s):  
Yi Hua Mao ◽  
Meng Bo Zhang ◽  
Ning Bo Yao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Real Estate ◽  
Housing Market ◽  
Bp Neural Network ◽  
Housing Demand ◽  
Forecasting Model ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
The Impact

Hangzhou, the capital of Zhejiang province and a famous scenic tourist city in China, goes at the forefront of the country for its high real estate prices, which hold a very important position of orientation to pricing in the real estate markets of the Yangtze River Delta region and of the whole country as well. The price trend of Hangzhou's real estate is even related to the sustainable development of the city. This paper uses the macro data on the housing market in Hangzhou during 1999-2012 to establish a forecasting model which is based on BP neural network of genetic algorithm optimization. With MATLAB software exploited for programming and simulation, the prediction made by the model about the housing demand in Hangzhou and the subsequent re-examination show that the model has high precision. But due to the impact of the national macro-control policies on housing market, the predictive value of some years may fluctuate to a certain extent.

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.

Improved Vibration Monitoring Through Optimization of Sensor Placements

2000 ◽  
Author(s):  
Mark D. Sensmeier ◽  
Kurt L. Nichol
Keyword(s):  
Genetic Algorithm ◽  
Selection Process ◽  
Dynamic Test ◽  
Software Tool ◽  
Vibration Monitoring ◽  
Optimization Approach ◽  
Algorithm Optimization ◽  
Turbine Engine ◽  
Mode Identification ◽  
Engine Components

A PC-based software tool has been developed which optimizes the placement of sensors for vibration monitoring. This tool, called Blade-OPS, incorporates a methodology that allows the instrumentation design engineer to make tradeoffs between mode identification, mode visibility, data integrity and geometry. It uses a genetic algorithm optimization approach that simulates the natural selection process to develop an optimum design. For the blade considered here, several instrumentation configurations were selected which yield an improved fitness rating relative to the baseline sensor locations which were selected without using rigorous optimization approach. Application of this capability is not limited to turbine engine components, but will be useful for any dynamic test where instrumentation is limited.

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 ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM

2020 ◽  
Author(s):  
Leonid Oliinyk ◽  
Stanislav Bazhan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Stochastic Matrices ◽  
Global Extremum ◽  
Modified Genetic Algorithm ◽  
Wide Range ◽  
Search Field ◽  
The Impact

Genetic algorithm is a method of optimization based on the concepts of natural selection and genetics. Genetic algorithms are used in software development, in artificial intelligence systems, a wide range of optimization problems and in other fields of knowledge.One of the important issues in the theory of genetic algorithms and their modified versions is the search for the best balance between performance and accuracy. The most difficult in this sense are problems where the fitness function in the search field has many local extremes and one global or several global extremes that coincide.The effectiveness of the genetic algorithm depends on various factors, such as the successful creation of the primary population. Also in the theory of genetic algorithms, recombination methods play an important role to obtain a better population of offspring. The aim of this work is to study some types of mutations using a modified genetic algorithm to find the minimum function of one variable.The article presents the results of research and analysis of the impact of some mutation procedures. Namely, the effect of mutation on the speed of achieving the solution of the problem of finding the global extremum of a function of one variable. For which a modified genetic algorithm is used, where the operators of the "generalized crossover" are stochastic matrices

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