scholarly journals Engineering by Fundamental Elements of Evolution

2008 ◽  
Author(s):  
Or Yogev ◽  
Andrew A. Shapiro ◽  
Erik K. Antonsson
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Growth And Development ◽  
Growth Process ◽  
Three Dimensional ◽  
Fitness Evaluation ◽  
Artificial Cell

The method presented in this note mimics two fundamental mechanisms from nature, growth, and development, for the synthesis of new three-dimensional structures. The structures were synthesized to support a load generated by a wind. Every structure grows from a single artificial cell following a set of genes, encoded in an artificial genome shared by all cells. Genes are a set of commands that control the growth process. Genes are regulated by interaction with the environment. The environment is both external and internal to the structure. The performance each structure is measured by its ability to hold the load and other additional engineering criteria. A population of structures is evolved using a genetic algorithm, which alters the genome of two mating individuals. We will present evolved phenotypes with high degrees of modularity and symmetry which evolved according to engineering criteria. Neither one of these two characteristics has been directly imposed as the fitness evaluation, but rather spontaneously emerge as a consequence of natural selection. We will argue that the types of rules we are using in this model are not biased toward any of these characteristics, but rather basic rules for growth and development.

scholarly journals Effect of RBD mutation (Y453F) in spike glycoprotein of SARS-CoV-2 on neutralizing antibody affinity

2020 ◽  
Author(s):  
Takuma Hayashi ◽  
Nobuo Yaegashi ◽  
Ikuo Konishi
Keyword(s):  
Experimental Study ◽  
Monoclonal Antibodies ◽  
Natural Selection ◽  
Structural Analysis ◽  
Growth Process ◽  
Three Dimensional ◽  
Neutralizing Antibody ◽  
Antibody Affinity ◽  
Protein Structural Analysis

AbstractNatural selection “adaptation” in the coronavirus can occur during coronavirus amplification in vivo in farmed minks. Natural selection in such viruses is observed by introduction of mutations in SARS- CoV-2 that are not observed during the growth process in humans. Infection with a mutant (Y453F) of SARS-CoV-2 from farmed minks is known to widely spread among humans. We investigated the virological characteristics of this SARS-CoV-2 mutant (Y453F) using three-dimensional protein structural analysis. Our experimental study suggests that virus variants with the Y453F mutation partially escaped detection by four neutralizing monoclonal antibodies. The spread of SARS-CoV-2 variants mediated by millions of infected farmed minks is uncontrolled; consequently, raising a concern that infection of SARS-CoV-2 mutants that cause serious symptoms in humans may spread globally.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

Analysis of SiC CVD Growth in a Horizontal Hot-Wall Reactor by Experiment and 3D Modelling

2007 ◽  
Vol 556-557 ◽  
pp. 61-64
Author(s):  
Y. Shishkin ◽  
Rachael L. Myers-Ward ◽  
Stephen E. Saddow ◽  
Alexander Galyukov ◽  
A.N. Vorob'ev ◽  
...  
Keyword(s):  
Growth Rate ◽  
Growth Process ◽  
Three Dimensional ◽  
Chemical Species ◽  
3D Modelling ◽  
Complete Model ◽  
Cvd Growth ◽  
Dimensional Simulation ◽  
Insight Into ◽  
Hot Zone

A fully-comprehensive three-dimensional simulation of a CVD epitaxial growth process has been undertaken and is reported here. Based on a previously developed simulation platform, which connects fluid dynamics and thermal temperature profiling with chemical species kinetics, a complete model of the reaction process in a low pressure hot-wall CVD reactor has been developed. Close agreement between the growth rate observed experimentally and simulated theoretically has been achieved. Such an approach should provide the researcher with sufficient insight into the expected growth rate in the reactor as well as any variations in growth across the hot zone.

An Interactive Visualization of Genetic Algorithm on 2-D Graph

2012 ◽  
Vol 4 (1) ◽  
pp. 34-54 ◽  
Author(s):  
Humera Farooq ◽  
Nordin Zakaria ◽  
Muhammad Tariq Siddique
Keyword(s):  
Genetic Algorithm ◽  
Growth Process ◽  
Search Space ◽  
Human Intervention ◽  
Branching Structures ◽  
Multidimensional Search ◽  
L System ◽  
User Fatigue ◽  
Search Data ◽  
Selection Of

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

scholarly journals Calibration of Kinematic Parameters of Robot Arm Using Laser Tracking System: Compensation for Non-Geometric Errors by Neural Networks and Selection of Optimal Measuring Points by Genetic Algorithm

2012 ◽  
Vol 6 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Seiji Aoyagi ◽  
◽  
Masato Suzuki ◽  
Tomokazu Takahashi ◽  
Jun Fujioka ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Tracking System ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Geometric Parameters ◽  
Geometric Errors ◽  
Robot Arm ◽  
Positioning Accuracy ◽  
Laser Tracking

Offline teaching based on high positioning accuracy of a robot arm is desired to take the place of manual teaching. In offline teaching, joint angles are calculated using a kinematic model of the robot arm. However, a nominal kinematic model does not consider the errors arising in manufacturing or assembly, not to mention the non-geometric errors arising in gear transmission, arm compliance, etc. Therefore, a method of precisely calibrating the parameters in a kinematic model is required. For this purpose, it is necessary to measure the three-dimensional (3-D) absolute position of the tip of a robot arm. In this paper, a laser tracking system is employed as the measurement apparatus. The geometric parameters in the robot kinematic model are calibrated by minimizing errors between the measured positions and the predicted ones based on the model. The residual errors caused by non-geometric parameters are further reduced by using neural networks, realizing high positioning accuracy of sub-millimeter order. To speed up the calibration process, a smaller number of measuring points is preferable. Optimal measuring points, which realize high positioning accuracy while remaining small in number, are selected using Genetic Algorithm (GA).

scholarly journals Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2814
Author(s):  
Mohammad Hassan Dehghanipour ◽  
Hojat Karami ◽  
Hamidreza Ghazvinian ◽  
Zahra Kalantari ◽  
Amir Hossein Dehghanipour
Keyword(s):  
Genetic Algorithm ◽  
Water Resource Management ◽  
Three Dimensional ◽  
Climatic Conditions ◽  
Pan Evaporation ◽  
Linear Quadratic ◽  
Climate Type ◽  
Method Error ◽  
Basic Relationship ◽  
Quadratic Relationship

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

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