Immune Programming Applications in Image Segmentation

2011 ◽  
pp. 304-321
Author(s):  
Xiaojun Bi
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Theoretical Analysis ◽  
Evolutionary Algorithm ◽  
Optimization Algorithms ◽  
Convergence Speed ◽  
Experimental Results ◽  
Intelligent Algorithms ◽  
Slow Convergence ◽  
Immune Programming

In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete the task of image segmentation. Traditional evolutionary algorithm represented by Genetic Algorithm is an efficient approach for image segmentation, but in the practical application, there are many problems such as the slow convergence speed of evolutionary algorithm and premature convergence, which have greatly constrained the application. The goal of introducing immunity into the existing intelligent algorithms is to utilize some characteristics and knowledge in the pending problems for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Theoretical analysis and experimental results show that immune programming outperforms the existing optimization algorithms in global convergence speed and is conducive to alleviating the degeneration phenomenon. Theoretical analysis and experimental results show that immune programming has better global optimization and outperforms the existing optimization algorithms in alleviating the degeneration phenomenon. It is a feasible and effective method of image segmentation.

Optimizing Fourier Series Based Gaits for Modular Robots Using an Evolutionary Algorithm

2013 ◽  
Author(s):  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Genetic Algorithm ◽  
Fourier Series ◽  
Evolutionary Algorithm ◽  
Periodic Motion ◽  
Degrees Of Freedom ◽  
Series Representation ◽  
Robotic System ◽  
Experimental Results ◽  
New Method ◽  
Modular Robots

A new method of controlling and optimizing robotic gaits for a modular robotic system is presented in this paper. A robotic gait is implemented on a robotic system consisting of three Mobot modules for a total of twelve degrees of freedom using a Fourier series representation for the periodic motion of each joint. The gait implementation allows robotic modules to perform synchronized gaits with little or no communication with each other making it scalable to increasing numbers of modules. The coefficients of the Fourier series are optimized by a genetic algorithm to find gaits which move the robot cluster quickly and efficiently across flat terrain. Simulated and experimental results show that the optimized gaits can have over twice as much speed as randomly generated gaits.

An Improved Genetic Algorithm for Digital Facility Layout Optimization Oriented Multi-Species and Variable-Batch Production Mode

2013 ◽  
Vol 694-697 ◽  
pp. 3632-3635
Author(s):  
Dao Guo Li ◽  
Zhao Xia Chen
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Batch Production ◽  
Production Mode ◽  
Slow Convergence ◽  
Weak Points

When solving facility layout problem for the digital workshop to optimize the production, the traditional genetic algorithm has its flaws with slow convergence speed and that the accuracy of the optimal solution is not ideal. This paper analyzes those weak points and proposed an improved genetic algorithm according to the characteristics of multi-species and variable-batch production mode. The proposed approach improved the convergence speed and the accuracy of the optimal solution. The presented model of GA also has been tested and verified by simulation.

Momentum Factor Constant Modulus Algorithm and Theory

2012 ◽  
Vol 263-266 ◽  
pp. 1058-1061
Author(s):  
Heng Yang ◽  
Jing Wang ◽  
Jing Guan ◽  
Wei Lu
Keyword(s):  
Theoretical Analysis ◽  
Blind Equalization ◽  
Weight Vector ◽  
Convergence Speed ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Slow Convergence ◽  
Iteration Formula ◽  
Equalization Algorithm ◽  
Factor Constant

The traditional constant modulus algorithm (CMA) has the disadvantage of slow convergence in blind equalization algorithm. This paper studied one improved algorithm based on momentum factor constant modulus algorithm(MCMA) to solve this problem, momentum factor was added to the weight vector iteration formula of CMA to improve the convergence speed. theoretical analysis and simulation showed that: in the case of the same equalization effect, the MCMA converges faster than the traditional constant modulus algorithm, and also different momentum factors have different convergence effects. The larger the momentum factor , the better convergence effect in the defined domain of the momentum factor.

Review of Task Scheduling Algorithm Research in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 3236-3239 ◽  
Author(s):  
Mei Geng Huang ◽  
Zhi Qi Ou
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm

2017 ◽  
Vol 24 (1) ◽  
pp. 367-373 ◽  
Author(s):  
Shibo Xi ◽  
Lucas Santiago Borgna ◽  
Lirong Zheng ◽  
Yonghua Du ◽  
Tiandou Hu
Keyword(s):  
Genetic Algorithm ◽  
Synchrotron Radiation ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Open Source ◽  
Light Source ◽  
Optimization Algorithms ◽  
Beijing Synchrotron Radiation Facility ◽  
On Line ◽  
Synchrotron Light

In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.

scholarly journals Genetic algorithm for optimization in adaptive bus signal priority control

2012 ◽  
Vol 3 (1) ◽  
pp. 35-43
Author(s):  
Tran Vu TU ◽  
Kazushi SANO
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Computation Time ◽  
Signalized Intersections ◽  
Convergence Speed ◽  
Rate Performance ◽  
Improved Genetic Algorithm ◽  
Slow Convergence ◽  
Bus Signal Priority ◽  
Compensation Rule

This paper firstly proposes an improved genetic algorithm (GA) for optimization in adaptive bus signal priority control at signalized intersections. Unlike conventional genetic algorithms with slow convergence speed, this algorithm can increase the convergence speed by utilizing the compensation rule between consecutive signal cycles to narrow new possible generated population spaces. Secondly, the paper would like to present a way to apply the algorithm to a simple adaptive bus signal priority control as well as compare how much the computation time is saved when applying the improved algorithm. Then the research thirdly investigates the efficiency of the proposed algorithm under various flow rate situations. The results show that the improved genetic algorithm can reduce the computation time considerably, by up to 48.39% for the studied case.  With high saturation degrees on the cross street, the convergence rate performance of the improved genetic algorithm is significantly good. The figure can be up to 36.2% when compared with the convergence rate of the conventional GA.

scholarly journals A coarse-grained parallelization of genetic algorithms

2018 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Muhamad Radzi Rathomi ◽  
Reza Pulungan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Message Passing ◽  
Optimization Problems ◽  
Convergence Speed ◽  
Experimental Results ◽  
Coarse Grained ◽  
Speed Up ◽  
Multiple Threads ◽  
Algorithm Parallelization

Genetic algorithms are frequently used to solve optimization problems. However, the problems become increasingly complex and time consuming. One solution to speed up the genetic algorithm processing is to use parallelization. The proposed parallelization method is coarse-grained and employs two levels of parallelization: message passing with MPI and Single Instruction Multiple Threads with GPU. Experimental results show that the accuracy of the proposed approach is similar to the sequential genetic algorithm. Parallelization with coarse-grained method, however, can improve the processing and convergence speed of genetic algorithms.

Optimization of Transit Hub Terminals Based on Improved Adaptive Genetic Algorithm

2014 ◽  
Vol 488-489 ◽  
pp. 942-946
Author(s):  
Chun Mei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Population Diversity ◽  
Convergence Speed ◽  
Adaptive Genetic Algorithm ◽  
Allocation Model ◽  
Slow Convergence ◽  
Optimized Allocation ◽  
The Individual ◽  
Crossover And Mutation

In this paper, how to design the layout of transit hub terminals is discussed, and an optimized allocation model about bus lines and bus terminals is established. In order to address the slow convergence of adaptive genetic algorithm, an index that indicates population diversity degree is introduced to adjust the individual crossover and mutation rate. This improved adaptive genetic algorithm is applied for the allocation model and an example is used to validate its efficiency. Results show that it is a promising approach and can improve the convergence speed.

An Effective Evolutionary Algorithm for Analyzing a Realiability of Geotechnical Engineering

2011 ◽  
Vol 105-107 ◽  
pp. 1528-1533
Author(s):  
Wei Zeng ◽  
Kai Wen ◽  
Bao Quan Zhao ◽  
Guang Cheng Zhang ◽  
San You Zeng
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Reliability Index ◽  
Geotechnical Engineering ◽  
Experimental Results ◽  
The Real ◽  
Real Coded Genetic Algorithm

The reliability index is not only nonlinear but also continuous, so we design the real coded genetic algorithm to improve the performance of the algorithm. The experimental results indicate that our method is 10 times faster than the binary-coded genetic algorithm, more accurate and stable than other methods.

scholarly journals An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Xian Zhang ◽  
Xiao-Yi Qian ◽  
Hui-Deng Peng ◽  
Jian-Hui Wang
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Convergence Rate ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Evolutionary Process ◽  
Experimental Results ◽  
Quantum Genetic Algorithm

For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. AndHεgate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved byMarkovchain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.

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