Genetic Algorithms and Random Keys for Sequencing and Optimization

In RFID technology, communication is based on random numbers, and the numbers used there are pseudo-random too (PRN). As for the PRN, it is generated by the computational tool that creates a sequence of numbers that are generally not related. In cryptography, we usually need to generate the encrypted and decrypted keys, so that we can use the genetic algorithm (GA) to find and present those keys. In this paper, the authors use the GA to find the random keys based on GA operators. The results of this generation attempt are tested through five statistical tests by which they try to determine the keys that are mostly responsible for message-encryption.

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Plate Eigenfrequency Optimization with Genetic Algorithms and Random Keys

Proceedings of the Ninth International Conference on Computational Structures Technology ◽

10.4203/ccp.88.73 ◽

2009 ◽

Author(s):

J.F. Aguilar Madeira ◽

H.L. Pina ◽

H.C. Rodrigues

Keyword(s):

Genetic Algorithms ◽

Random Keys ◽

Eigenfrequency Optimization

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Hierarchical time-optimal control of a continuous copolymerization reactor during start-up or grade change operation using genetic algorithms

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00186-5 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S1037-S1042

Author(s):

H Moo

Keyword(s):

Optimal Control ◽

Genetic Algorithms ◽

Time Optimal Control ◽

Grade Change ◽

Start Up ◽

Time Optimal ◽

Change Operation ◽

Grade Change Operation

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Genetic Algorithms—a Tool for OR?

Journal of the Operational Research Society ◽

10.1057/palgrave.jors.0470406 ◽

1996 ◽

Vol 47 (4) ◽

pp. 550-561 ◽

Cited By ~ 2

Author(s):

Kathryn A Dowsland

Keyword(s):

Genetic Algorithms

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Road Horizontal Alignment Design Method for Continuous Loop Area in Autonomous Vehicles Proving Ground Based on Genetic Algorithms

CICTP 2020 ◽

10.1061/9780784483053.163 ◽

2020 ◽

Author(s):

Junyi Chen ◽

Rubing Li ◽

Xingyu Xing ◽

Lu Xiong

Keyword(s):

Genetic Algorithms ◽

Autonomous Vehicles ◽

Design Method ◽

Horizontal Alignment ◽

Loop Area

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

Al-Kitab Journal for Pure Sciences ◽

10.32441/kjps.v1i1.87 ◽

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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