Binary trie coding scheme: an intelligent genetic algorithm avoiding premature convergence

2012 ◽  
Vol 90 (5) ◽  
pp. 881-902 ◽  
Author(s):  
Sunanda Gupta ◽  
M. L. Garg
Keyword(s):  
Genetic Algorithm ◽  
Premature Convergence ◽  
Coding Scheme ◽  
Intelligent Genetic Algorithm

Network Coding Optimization Method Research Based on Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2059-2062
Author(s):  
Hong Yan Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Optimization Method ◽  
Premature Convergence ◽  
Initial Population ◽  
Running Time ◽  
Coding Scheme ◽  
Algorithm Search

Network coding optimization method research based on genetic algorithm applies network coding technology in monophyletic multicast network. After reaching network multicast rate, find link coding scheme which makes the minimum of the total number of network coding. Moreover, it makes the analysis and improvement for general genetic algorithm’s defects in network coding link optimization such as rare individual successful decoded by randomly generated initial population strategies, reduced algorithm search ability, premature convergence of genetic algorithm and long algorithm running time.

scholarly journals Utilizing Model Knowledge for Design Developed Genetic Algorithm to Solving Problem

2018 ◽  
Vol 3 (2) ◽  
pp. 172
Author(s):  
Hamidreza Salmani mojaveri
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Machines ◽  
Job Shop ◽  
Optimization Problems ◽  
Premature Convergence ◽  
Local Optimum ◽  
Scheduling Problems ◽  
Algorithm Implementation

One of the discussed topics in scheduling problems is Dynamic Flexible Job Shop with Parallel Machines (FDJSPM). Surveys show that this problem because of its concave and nonlinear nature usually has several local optimums. Some of the scheduling problems researchers think that genetic algorithms (GA) are appropriate approach to solve optimization problems of this kind. But researches show that one of the disadvantages of classical genetic algorithms is premature convergence and the probability of trap into the local optimum. Considering these facts, in present research, represented a developed genetic algorithm that its controlling parameters change during algorithm implementation and optimization process. This approach decreases the probability of premature convergence and trap into the local optimum. The several experiments were done show that the priority of proposed procedure of solving in field of the quality of obtained solution and convergence speed toward other present procedure.

Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem

2013 ◽  
pp. 8-16
Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li
Keyword(s):  
Genetic Algorithm ◽  
Sexual Reproduction ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Function Optimization ◽  
Premature Convergence ◽  
Multimodal Functions ◽  
Multimodal Function Optimization ◽  
Multimodal Function

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.

Sign in / Sign up

Export Citation Format

Share Document