scholarly journals Evolutionary Computation Techniques for Predicting Atmospheric Corrosion

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Amine Marref ◽  
Saleh Basalamah ◽  
Rami Al-Ghamdi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Corrosion Rate ◽  
Genetic Programming ◽  
Atmospheric Corrosion ◽  
Structural Integrity ◽  
Good Accuracy ◽  
Residual Life ◽  
Vital Role ◽  
Engineering Structures

Corrosion occurs in many engineering structures such as bridges, pipelines, and refineries and leads to the destruction of materials in a gradual manner and thus shortening their lifespan. It is therefore crucial to assess the structural integrity of engineering structures which are approaching or exceeding their designed lifespan in order to ensure their correct functioning, for example, carrying ability and safety. An understanding of corrosion and an ability to predict corrosion rate of a material in a particular environment plays a vital role in evaluating the residual life of the material. In this paper we investigate the use of genetic programming and genetic algorithms in the derivation of corrosion-rate expressions for steel and zinc. Genetic programming is used to automatically evolve corrosion-rate expressions while a genetic algorithm is used to evolve the parameters of an already engineered corrosion-rate expression. We show that both evolutionary techniques yield corrosion-rate expressions that have good accuracy.

Evolutionary Computation and Genetic Algorithms

2011 ◽  
pp. 477-481
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Natural Selection ◽  
Genetic Programming ◽  
Sexual Reproduction ◽  
Evolutionary Computation ◽  
Evolutionary Biology ◽  
Approximate Solutions ◽  
Biologically Inspired ◽  
Genetic Inheritance

A genetic algorithm (GA) is a procedure used to find approximate solutions to search problems through the application of the principles of evolutionary biology. Genetic algorithms use biologically inspired techniques, such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover). Along with genetic programming (GP), they are one of the main classes of genetic and evolutionary computation (GEC) methodologies.

Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

2012 ◽  
pp. 125-144
Author(s):  
Arpit Tripathi ◽  
Pulkit Gupta ◽  
Aditya Trivedi ◽  
Rahul Kala
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Ease Of Use ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimal Placement ◽  
Coverage Area ◽  
Node Placement

The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.

Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

2011 ◽  
Vol 7 (2) ◽  
pp. 63-83 ◽  
Author(s):  
Arpit Tripathi ◽  
Pulkit Gupta ◽  
Aditya Trivedi ◽  
Rahul Kala
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Genetic Programming ◽  
Ease Of Use ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Optimal Placement ◽  
Coverage Area ◽  
Node Placement

The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

Research on Prediction of Carbon Steel in SO2 Atmospheric Corrosion Using Grey Model

2014 ◽  
Vol 507 ◽  
pp. 258-262
Author(s):  
Ping Wang ◽  
Zhao Hui Yin ◽  
Han Tao Ren ◽  
Song Xu
Keyword(s):  
Carbon Steel ◽  
Corrosion Rate ◽  
Prediction Model ◽  
Relative Error ◽  
Atmospheric Corrosion ◽  
Grey Model ◽  
Fitting Accuracy

The rate of carbon steel in SO2 Atmospheric Corrosion was modeled by grey model GM (1, 1). The accuracy and rationality of prediction model have been evaluated. The result indicated that the model had a better fitting accuracy. By comparing the calculated values with a predicted atmospheric corrosion rate of carbon steel after 264h, it showed that its relative error has been just 0.5619% which had higher forecast reliability.

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