Function Optimization and Image Matching Based on Improved Adaptive Genetic Algorithm

2012 ◽  
Vol 4 (19) ◽  
pp. 57-64
Author(s):  
Jiangbo Huang
Keyword(s):  
Genetic Algorithm ◽  
Image Matching ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm

Approach for Solving Nonlinear Equation Group Based on Adaptive Genetic Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1636-1639
Author(s):  
Hong Lian Shen ◽  
Feng Lin Cheng ◽  
Huan Ru Ren
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Accurate Solution ◽  
Initial Solution ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm ◽  
Time Efficiency ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Equation Group

A numeric method of solving nonlinear equation group is proposed. The problem of solving nonlinear equation group is equivalently changed to the problem of function optimization, and then a solution is obtained by adaptive genetic algorithm, considering it as the initial solution of Levenberg-Marquardt algorithm, a more accurate solution can be obtained, as a result, time efficiency is improved.

Robot View Navigation Based on Level-Divided Strategy

2013 ◽  
Vol 753-755 ◽  
pp. 3108-3111
Author(s):  
Yin Bing Li
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Image Matching ◽  
Search Strategy ◽  
Detection Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Exact Matching ◽  
Matching Accuracy ◽  
Similarity Detection ◽  
Time Requirements

In allusion to the colored image matching characteristic in the system of robot view navigation, SSDA (the sequential similarity detection algorithm) is improved and adaptive genetic algorithm is brought in; meanwhile, level-divided search strategy connective with rough and exact matching. The improved algorithm can enhance the image matching speed with no matching accuracy reduced, so that real-time requirements of robot view navigation can be met and robot view navigation will be of preferable robustness.

An Improved Fuzzy Adaptive Genetic Algorithm for Function Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 2598-2601
Author(s):  
Lan Yao ◽  
Yu Lian Jiang ◽  
Jian Xiao
Keyword(s):  
Genetic Algorithm ◽  
Population Diversity ◽  
Function Optimization ◽  
Adaptive Genetic Algorithm ◽  
Adaptive Selection ◽  
Mutation Probability ◽  
Individual Fitness ◽  
Fitness Value ◽  
Crossover Probability ◽  
Fuzzy Adaptive

The critical operators for genetic algorithms to avoid premature and improve globe convergence is the adaptive selection of crossover probability and mutation probability. This paper proposed an improved fuzzy adaptive genetic algorithm in which the variance of population and individual fitness value are used to measure the overall population diversity and individual difference, meanwhile, both of them are applied to design fuzzy reference system for adaptively estimation of crossover probability and mutation probability. Simulation results of function optimization show that the new algorithm can converge faster and is more effective at avoiding premature convergence in comparison with standard genetic algorithm.

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