Multiclass SVM Design and Parameter Selection with Genetic Algorithms

2006 ◽  
Author(s):  
Ana Lorena ◽  
Andre P. L. F. De Carvalho
Keyword(s):  
Genetic Algorithms ◽  
Parameter Selection ◽  
Multiclass Svm

Genetic algorithms for automatic algorithm and parameter selection in ATR applications

1998 ◽  
Author(s):  
Paul G. Ducksbury ◽  
Margaret J. Varga ◽  
P. J. Kent ◽  
Stephen B. Foulkes ◽  
David M. Booth
Keyword(s):  
Genetic Algorithms ◽  
Parameter Selection ◽  
Automatic Algorithm

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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