Adaptive genetic algorithm for optimal selection of non-uniform code based on Euclidean distance

2009 ◽  
Author(s):  
Zhang Mingbo ◽  
Luo Feng ◽  
Wu Shunjun
Keyword(s):  
Genetic Algorithm ◽  
Euclidean Distance ◽  
Adaptive Genetic Algorithm ◽  
Optimal Selection ◽  
Uniform Code ◽  
Selection Of

scholarly journals An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression

2015 ◽  
Vol 16 (Suppl 18) ◽  
pp. S1 ◽  
Author(s):  
Luke Vandewater ◽  
Vladimir Brusic ◽  
William Wilson ◽  
Lance Macaulay ◽  
Ping Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Genetic Algorithm ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Adaptive Genetic Algorithm ◽  
Selection Of

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

2011 ◽  
Vol 49 (10) ◽  
pp. 3045-3068 ◽  
Author(s):  
Jitender K. Rai ◽  
Daniel Brand ◽  
Mohammed Slama ◽  
Paul Xirouchakis
Keyword(s):  
Genetic Algorithm ◽  
Cutting Parameters ◽  
Optimal Selection ◽  
Milling Operations ◽  
Selection Of

One Optimizing Method for Moments of Inertia Applied with Improved Adaptive Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 54-57
Author(s):  
Heng Hui Sun ◽  
Min Zhou Luo ◽  
Xiang Dong ◽  
Shi Hong Zha
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Optimal Model ◽  
Moments Of Inertia ◽  
New Strategy ◽  
Optimizing Model ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Selection Of

In this paper, a new optimizing method for the moments of inertia of a mechanical structure was advanced. First, a new optimal model for the moments of inertia was advanced, which only involved with single objective and single variable, in order to reduce the calculating complexity of traditional multi-objective and multi-constrained optimizing model for the moments of inertia; Then, a new strategy for the probability selection of the crossover and mutation operation was improved to form the IAGA. The calculating results proved that, comparing to the Standard Genetic Algorithm (SGA), the IAGA improved in this paper had the advantage of converging faster, more powerfully searching, and less possible of falling into the local optimum. By that, the feasibility of the method advanced in this paper was demonstrated.

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