Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions

2021 ◽  
Vol 125 (15) ◽  
pp. 3824-3829
Author(s):  
Xiao Ru ◽  
Zijing Lin
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Space Dimension ◽  
Search Space ◽  
Peptide Structure ◽  
Reduction Scheme

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

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.

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).

Genetic Algorithm-Based Searching Method for Piping Path Routing in Coal-Fired Boiler Buildings

2011 ◽  
Author(s):  
Naohiro Kusumi ◽  
David E. Goldberg ◽  
Noriyuki Ichinose
Keyword(s):  
Genetic Algorithm ◽  
Computer Aided Design ◽  
Steel Structures ◽  
Search Space ◽  
Design System ◽  
Dynamic Programming Method ◽  
Plant Design ◽  
Technical Specifications ◽  
The Many ◽  
Searching Method

Power plant design using digital engineering based on 3-D computer-aided design has become a mainstream technology because of possessing higher speed and improvement in design accuracy. To take a coal-fired boiler building as an example, it has many complex structures with several million parts including the boiler itself, large fans, steel structures, and piping in varying sizes. Therefore, it is not easy to maintain integrity of the whole design throughout all the many engineering processes. We have developed a smart design system for coal-fired boiler buildings to solve the integrity problem. This system is capable of creating and allocating 3-D models automatically in accordance with various technical specifications and engineering rules. Lately, however, there has been a growing demand for more effectiveness of the developed system. We have begun to look into the feasibility of further improvements of the system function. The first point to note, when considering effectiveness, is the piping path routing process in the coal-fired boiler building. The quantity of piping is large, and it has a considerable impact on performance of the whole plant because hot steam is fed into the steam turbine and cold steam is taken from it through the piping. A considerable number of studies have been made on automatic searching methods of piping path routing. Although, the decision of piping path routing by using the Dynamic Programming method is most commonly, a previously decided routing becomes an interference object because of the single searching method. Therefore, basically, the later the order of the routing becomes, the longer the length of the routing becomes. To overcome this problem, in this paper we have proposed a new searching method based on the Genetic Algorithm (GA). The GA is a multipoint searching algorithm based on the mechanics of natural selection and natural genetics. Virtual prohibited cells are introduced into the proposed search method as a new idea. The virtual prohibited cells are located in a search space. The different paths are generated by avoiding the virtual prohibited cells while searching for the piping path routing. The optimum locations of the prohibited cells which are expressed in a genotype are obtained by using the GA in order to get a lot of paths independent of the order of the routing. The proposed method was evaluated using a simple searching problem. The results showed that many effective paths are generated by making the virtual prohibited cells.

scholarly journals Parameter space dimension reduction of an adaptive interpolator during multidimensional signal differential compression

2019 ◽  
pp. 23-30
Author(s):  
A I Maksimov ◽  
M V Gashnikov
Keyword(s):  
Dimension Reduction ◽  
Parameter Space ◽  
Compression Ratio ◽  
Space Dimension ◽  
Computational Experiments ◽  
Parameter Range ◽  
Compression Method ◽  
Multidimensional Signals ◽  
Multidimensional Signal ◽  
Adaptive Interpolator

We propose a new adaptive multidimensional signal interpolator for differential compression tasks. To increase the efficiency of interpolation, we optimize its parameters space by the minimum absolute interpolation error criterion. To reduce the complexity of interpolation optimization, we reduce the dimension of its parameter range. The correspondence between signal samples in a local neighbourhood is parameterized. Besides, we compare several methods for such parameterization. The developed adaptive interpolator is embedded in the differential compression method. Computational experiments on real multidimensional signals confirm that the use of the proposed interpolator can increase the compression ratio.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

Alien Genetic Algorithm for Exploration of Search Space

2010 ◽  
Author(s):  
Narendra Patel ◽  
Nitin Padhiyar ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
Suchismita Roy
Keyword(s):  
Genetic Algorithm ◽  
Search Space
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