Interpretation of Binary Strings as Security Protocols for their Evolution by means of Genetic Algorithms

2007 ◽  
Author(s):  
Luis Zarza ◽  
Josep Pegueroles ◽  
Miguel Soriano
Keyword(s):  
Genetic Algorithms ◽  
Security Protocols ◽  
Binary Strings

Using Genetic Algorithms to Explore Pattern Recognition in the Immune System

1993 ◽  
Vol 1 (3) ◽  
pp. 191-211 ◽  
Author(s):  
Stephanie Forrest ◽  
Brenda Javornik ◽  
Robert E. Smith ◽  
Alan S. Perelson
Keyword(s):  
Pattern Recognition ◽  
Genetic Algorithms ◽  
Immune System ◽  
System Model ◽  
Central Component ◽  
Simulation Experiments ◽  
Immune Systems ◽  
Fitness Sharing ◽  
The Individual ◽  
Binary Strings

This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

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).

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator
Sign in / Sign up

Export Citation Format

Share Document