scholarly journals Image Segmentation Using Genetic Algorithms

2021 ◽  
Vol 4 ◽  
pp. 52-55
Author(s):  
Semen Gorokhovskyi ◽  
Andrii Moroz
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Image Segmentation ◽  
Optimization Problems ◽  
Random Search ◽  
Solution Space ◽  
Optimization Techniques ◽  
Combinatorial Search ◽  
Good Tool ◽  
Data Set

Image segmentation is a crucial step in the image processing and analysis process. Image segmentation is the process of splitting one image into many segments. Image segmentation divides images into segments that are more representative and easier to examine. Individual surfaces or items can be used as such pieces. The process of image segmentation is used to locate objects and their boundaries.Genetic algorithms are stochastic search methods, the work of which is taken from the genetic laws, natural selection, and evolution of organisms. Their main attractive feature is the ability to solve complex problems of combinatorial search effectively, because the parallel study of solutions, largely eliminates the possibility of staying on the local optimal solution rather than finding a global one.The point of using genetic algorithms is that each pixel is grouped with other pixels using a distance function based on both local and global already calculated segments. Almost every image segmentation algorithm contains parameters that are used to control the segmentation results; the genetic system can dynamically change parameters to achieve the best performance.Similarly to image sequencing, to optimize several parameters in the process, multi-targeted genetic algorithms were used, which enabled finding a diverse collection of solutions with more variables. Multi- targeted Genetic Algorithm (MTGA) is a guided random search method that consists of optimization techniques. It can solve multi-targeted optimization problems and explore different parts of the solution space. As a result, a diversified collection of solutions can be found, with more variables that can be optimized at the same time. In this article several MTGA were used and compared.Genetic algorithms are a good tool for image processing in the absence of a high-quality labeled data set, which is either a result of the long work of many researchers or the contribution of large sums of money to obtain an array of data from external sources.In this article, we will use genetic algorithms to solve the problem of image segmentation.

A Hybrid Genetic-Direct Search Algorithm for the Shape Optimization of Solid C-Frame Cross-Sections

2000 ◽  
Author(s):  
Ashraf O. Nassef ◽  
Hesham A. Hegazi ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithms ◽  
Shape Optimization ◽  
Cross Section ◽  
Random Search ◽  
Direct Search ◽  
Search Method ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
The Cross ◽  
Real Coded Genetic Algorithms

Abstract The hybridization of different optimization methods have been used to find the optimum solution of design problems. While random search techniques, such as genetic algorithms and simulated annealing, have a high probability of achieving global optimality, they usually arrive at a near optimal solution due to their random nature. On the other hand direct search methods are efficient optimization techniques but linger in local minima if the objective function is multi-modal. This paper presents the optimization of C-frame cross-section using a hybrid optimization algorithm. Real coded genetic algorithms are used as a random search method, while Nelder-Mead is used as a direct search method, where the result of the genetic algorithm search is used as the starting point of direct search. Traditionally, the cross-section of C-frame belonged to a set of primitive shapes, which included I, T, trapezoidal, circular and rectangular sections. The cross-sectional shape is represented by a non-uniform rational B-Splines (NURBS) in order to give it a kind of shape flexibility. The results showed that the use of Nelder-Mead with Real coded Genetic Algorithms has been very significant in improving the optimum shape of a solid C-frame cross-section subjected to a combined tension and bending stresses. The hybrid optimization method could be extended to more complex shape optimization problems.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

Researches on Function Optimization Problems Based on Chaotic Immune Genetic Algorithms

2012 ◽  
Vol 616-618 ◽  
pp. 2064-2067
Author(s):  
Yong Gang Che ◽  
Chun Yu Xiao ◽  
Chao Hai Kang ◽  
Ying Ying Li ◽  
Li Ying Gong
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Chaotic Map ◽  
Solution Space ◽  
Population Diversity ◽  
Function Optimization ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Complex Functions

To solve the primary problems in genetic algorithms, such as slow convergence speed, poor local searching capability and easy prematurity, the immune mechanism is introduced into the genetic algorithm, and thus population diversity is maintained better, and the phenomena of premature convergence and oscillation are reduced. In order to compensate the defects of immune genetic algorithm, the Hénon chaotic map, which is introduced on the above basis, makes the generated initial population uniformly distributed in the solution space, eventually, the defect of data redundancy is reduced and the quality of evolution is improved. The proposed chaotic immune genetic algorithm is used to optimize the complex functions, and there is an analysis compared with the genetic algorithm and the immune genetic algorithm, the feasibility and effectiveness of the proposed algorithm are proved from the perspective of simulation experiments.

Applying Large-Scale Optimization to Evaluate Pavement Maintenance Alternatives for Low-Volume Roads using Genetic Algorithms

2018 ◽  
Vol 2672 (52) ◽  
pp. 205-215 ◽  
Author(s):  
Marwan Hafez ◽  
Khaled Ksaibati ◽  
Rebecca A. Atadero
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Pavement Performance ◽  
Large Scale Optimization ◽  
Maintenance Policy ◽  
Low Volume Roads ◽  
Low Volume ◽  
Scale Optimization

Over the last decade, significant progress has been made to customize the maintenance policies of low-volume roads (LVRs) to local needs and available resources. Low-cost treatments and surface repairs are extensively employed to reduce annual maintenance costs. Colorado Department of Transportation (CDOT) uses chip seals and thin overlays as the available treatment options applied to LVRs. However, the effectiveness of these treatments differs depending on the existing condition of pavements. Some surface treatments and light rehabilitations provide only short-term effectiveness. Multi-year optimization techniques can support decision makers with a set of optimal maintenance activities to achieve specific pavement performance targets. This study applies large-scale optimization to compare the current CDOT maintenance policy with an alternative strategy recommended for low-volume paved roads in Colorado. Genetic algorithms were applied in the optimization models because they are capable of resolving the computational complexity of optimization problems in a timely fashion. The optimized maintenance alternatives were comprehensively investigated for a LVR network in Colorado over a specific planning horizon. The specific optimization constraints and limitations prevailing in LVRs are addressed and introduced in the problem formulation of the optimization process. The results of both performance and cost analysis emphasize the effectiveness of the proposed maintenance strategy compared with the existing one. The alternative policy provides much more benefit-cost saving while preserving the overall pavement performance of the network. This approach is expected to be efficient to quantify the mid- and long-term financial impact of different treatment policies applied to LVRs within modest resources.

scholarly journals Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

2019 ◽  
Vol 29 (3) ◽  
pp. 150 ◽  
Author(s):  
Elham Jasim Mohammad
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Sem Image ◽  
Huge Data ◽  
And Performance ◽  
Biometric Information

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested.

scholarly journals On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach

VLSI Design ◽  
1996 ◽  
Vol 4 (3) ◽  
pp. 207-215 ◽  
Author(s):  
M. Srinivas ◽  
L. M. Patnaik
Keyword(s):  
Genetic Algorithms ◽  
Cost Function ◽  
Gradient Descent ◽  
Random Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Gradient Descent Methods ◽  
Test Vectors ◽  
The Cost ◽  
Optimal Signal

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger.To account for the relatively quick convergence of the gradient descent methods, we analyze the landscape of the COP-based cost function. We prove that the cost function is unimodal in the search space. This feature makes the cost function amenable to optimization by gradient-descent techniques as compared to random search methods such as Genetic Algorithms.

scholarly journals A Review of Deep-Learning-Based Medical Image Segmentation Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1224
Author(s):  
Xiangbin Liu ◽  
Liping Song ◽  
Shuai Liu ◽  
Yudong Zhang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Rapid Development ◽  
Medical Image Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Biomedical Image Processing ◽  
Data Set ◽  
Segmentation Methods

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research status and summarizing the three main methods of medical image segmentation and their own limitations, the future development direction is expanded. Based on the discussion of different pathological tissues and organs, the specificity between them and their classic segmentation algorithms are summarized. Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research. For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. The inaccurate segmentation results are unable to meet the actual clinical requirements. Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems.

Optimization Method RasID-GA for Numerical Constrained Optimization Problems

2007 ◽  
Vol 11 (5) ◽  
pp. 469-477 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Random Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Optimization Method ◽  
Penalty Functions ◽  
Objective Functions ◽  
Constrained Optimization Problems

This paper proposes Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) for constrained optimization. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for unconstrained optimization problems. In general, it is very difficult to find an optimal solution for constrained optimization problems because their feasible solution space is very limited and they should consider the objective functions and constraint conditions. The conventional constrained optimization methods usually use penalty functions to solve given problems. But, it is generally recognized that the penalty function is hard to handle in terms of the balance between penalty functions and objective functions. In this paper, we propose a constrained optimization method using RasID-GA, which solves given problems without using penalty functions. The proposed method is tested and compared with Evolution Strategy with Stochastic Ranking using well-known 11 benchmark problems with constraints. From the Simulation results, RasID-GA can find an optimal solution or approximate solutions without using penalty functions.

scholarly journals Inversion of seismic refraction data using genetic algorithms

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1715-1727 ◽  
Author(s):  
Fabio Boschetti ◽  
Mike C. Dentith ◽  
Ron D. List
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Seismic Refraction ◽  
Solution Space ◽  
Optimization Methods ◽  
Data Set ◽  
Weathered Layer ◽  
Seismic Refraction Data ◽  
Refraction Data

The use of genetic algorithms in geophysical inverse problems is a relatively recent development and offers many advantages in dealing with the nonlinearity inherent in such applications. However, in their application to specific problems, as with all algorithms, problems of implementation arise. After extensive numerical tests, we implemented a genetic algorithm to efficiently invert several sets of synthetic seismic refraction data. In particular, we aimed at overcoming one of the main problems in the application of genetic algorithms to geophysical problems: i.e., high dimensionality. The addition of a pseudo‐subspace method to the genetic algorithm, whereby the complexity and dimensionality of a problem is progressively increased during the inversion, improves the convergence of the process. The method allows the region of the solution space containing the global minimum to be quickly found. The use of local optimization methods at the last stage of the search further improves the quality of the inversion. The genetic algorithm has been tested on a field data set to determine the structure and base of the weathered layer (regolith) overlaying a basement of granite and greenstones in an Archaean terrain of Western Australia.

Crossover Strategy for Improved Solution Space Exploration With Genetic Algorithms

1998 ◽  
Author(s):  
Kurt S. Anderson ◽  
YuHung Hsu
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Single Point ◽  
Solution Space ◽  
Nonuniform Distribution ◽  
High Dimensional ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Entire Chromosome ◽  
Dimensional Optimization

Abstract The following paper presents a modified crossover operator to extend the exploration capability in Genetic Algorithms for high dimensional optimization problems. Traditional strategies apply crossover once on a pair of selected chromosomes to generate two offspring by randomly selecting a single crossover location within the chromosomal length. The proposed method applies crossover once on each separate gene (variable) instead of on the entire chromosome. To further accelerate exploration of the Genetic Algorithm, nonuniform distribution of the respective crossover position on each gene has also been studied. The empirical results show that Genetic Algorithms with the proposed crossover strategies can find optimal or near optimal solutions within fewer generations than traditional single point crossover.

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