An Improved Genetic Algorithm Based on Cellular Automata

2013 ◽  
Vol 340 ◽  
pp. 727-731
Author(s):  
Hong Tang ◽  
Yun Sheng Ge ◽  
Xiao Hai Pan ◽  
Shu Feng We
Keyword(s):  
Genetic Algorithm ◽  
Cellular Automata ◽  
Search Time ◽  
Target Function ◽  
Convergence Speed ◽  
Optimal Result ◽  
Improved Genetic Algorithm ◽  
Simple Genetic Algorithm

In order to overcome the drawbacks of Simple Genetic Algorithm such as cannot get the most optimal result, low convergence speed et al. Cellular Simple Genetic Algorithm-a new genetic algorithm based on Cellular Automata-is presented in this paper. Compared with the Simple Genetic Algorithm, the experiment results show the Cellular Simple Genetic Algorithm has remarkable advantages in following aspects: reducing the search-time and improving the precise of target function.

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.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Study of Multi-Project Conflicts by IGA Based on the Improvement of Fitness Function

2013 ◽  
Vol 756-759 ◽  
pp. 2768-2773
Author(s):  
Zhi Feng Lv ◽  
Xiang Dong Ma
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Selection Methods ◽  
Improved Genetic Algorithm ◽  
Resource Conflicts ◽  
Simple Genetic Algorithm ◽  
The Family ◽  
Family Orientation ◽  
Standard Genetic Algorithm ◽  
Mutation And Selection

In the multi-project resource conflicts exist in the application of standard genetic algorithm fitness function exist "premature" problem, Genetic algorithm can not find the convergence of these issue. Based on the above issues ,an improved genetic algorithm (IGA) are appropriate, From the fitness function, mutation and selection methods to improve two aspects are described, the Improved genetic algorithm for simple genetic algorithm has the advantage of generations of each evolution, offspring parent always retains the best individual to the "high-fitness model for the ancestors of the family orientation" search out better samples, and verified through experiments the effectiveness of the algorithm

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.

The Application of the Improved Genetic Algorithm in the Aeroengine Fault Diagnosis

2013 ◽  
Vol 846-847 ◽  
pp. 840-843
Author(s):  
Xiao Bo Liu ◽  
Jun Chao Tu ◽  
Liang Ni Shen
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Fitness Function ◽  
Convergence Speed ◽  
Fault Classification ◽  
Fault Identification ◽  
Improved Genetic Algorithm ◽  
Characteristic Parameters

A improved genetic algorithm is proposed based on a new fitness function in allusion to the problem that the traditional genetic algorithm is not fully consider the knowledge of the problem itself.The improved genetic algorithm is used to analyze the fault feature , to extract the fault and remove redundant characteristic parameters for the fault classification and calculation.The diagnosis example shows that the method has faster convergence speed and can be effective for fault identification.

Path Planning under Dynamic Threat Environment

2014 ◽  
Vol 543-547 ◽  
pp. 1790-1794 ◽  
Author(s):  
Wan Feng Ji ◽  
Hai Feng Xu ◽  
Guang Yuan Wang ◽  
Xiang Hong Yang
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Target Function ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Function Model ◽  
Dynamic Movement ◽  
Movement Characteristics ◽  
Path Planning Problem ◽  
Threat Environment

This paper studies the solution to combat aircrafts path planning problem in confrontational battle space. First of all, according to the dynamic movement characteristics of emergent threats, a path planning target function model is built based on dynamic threat; then based on the defects of basic genetic algorithm, a kind of improved genetic algorithm based on predatory search strategies is further designed; finally the model and the algorithm are tested effective by simulation verification.

scholarly journals Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Xu
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Convergence Speed ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Teaching Resources ◽  
College English ◽  
Conflict Problem ◽  
Fitness Value

In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses. Firstly, a variable-length decimal coding scheme satisfying the same course that can be scheduled at different times, different classrooms, and different teaching weeks per week is proposed, which fully considers the flexibility of classrooms and time arrangements of the course and makes the scheduling problem more reasonable. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. It is experimentally demonstrated that the designed algorithm not only has a faster convergence speed but also improves the diversity of individuals to a certain extent to enhance the search space and jump out of the local optimum. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm. At the same time, the use of the largest fuzzy pattern algorithm effectively solves the conflict problem of college English lesson scheduling, thereby improving the solution of college English lesson scheduling. Through the research of this article, the management system of college English course scheduling has been made more intelligent, and the rational allocation of teaching resources and the completion of education and teaching plans have been improved.

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