scholarly journals A Random Keys Based Genetic Algorithm for the Target Visitation Problem

2007 ◽  
pp. 389-397 ◽  
Author(s):  
Ashwin Arulselvan ◽  
Clayton W. Commander ◽  
Panos M. Pardalos
Keyword(s):  
Genetic Algorithm ◽  
Random Keys

A Key Establishment Attempt Based on Genetic Algorithms Applied to RFID Technologies

2021 ◽  
Vol 15 (3) ◽  
pp. 33-47
Author(s):  
Nabil Kannouf ◽  
Mohamed Labbi ◽  
Yassine Chahid ◽  
Mohammed Benabdellah ◽  
Abdelmalek Azizi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Statistical Tests ◽  
Random Numbers ◽  
Rfid Technology ◽  
Computational Tool ◽  
Key Establishment ◽  
Random Keys

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.

scholarly journals Problem solving of container loading using genetic algorithm based on modified random keys

2015 ◽  
Vol 4 (1) ◽  
pp. 190 ◽  
Author(s):  
Mohammad Sadegh Arefi ◽  
Hassan Rezaei
Keyword(s):  
Genetic Algorithm ◽  
Problem Solving ◽  
Evolutionary Algorithms ◽  
Global Optimum ◽  
Container Loading ◽  
Performance Time ◽  
Random Keys ◽  
Loading Problem ◽  
And Performance ◽  
Container Loading Problem

<p>This article presents a solution to the container loading problem. Container loading problem deals with how to put the cube boxes with different sizes in a container. Our proposed method is based on a particular kind of genetic algorithm based on biased random keys. In the proposed algorithm, we will face generations' extinction. Population decreases with time and with the staircase changes in the rate of elitism, the algorithm is guided towards the global optimum. Biased random keys in the proposed method are provided as discrete. The algorithm also provides the chromosomes that store more than one ability. In order to solve container loading using a placement strategy, due to the size of the boxes and containers, the containers are classified as small units and equal unites in size. Finally the algorithm presented in this paper was compared with three other methods that are based on evolutionary algorithms. The results show that the proposed algorithm has better performance in terms of results and performance time in relation to other methods.</p>

A Random Keys Genetic Algorithm Approach for Reconfiguration Problem in Distribution Power Networks

2019 ◽  
Author(s):  
Mateus Teixeira Magalhaes ◽  
Gabriel Brandao de Miranda ◽  
Luciana Brugiolo Goncalves ◽  
Lorenza Leao Oliveira Moreno ◽  
Stenio Sa Rosario Furtado Soares ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Power Networks ◽  
Random Keys ◽  
Genetic Algorithm Approach

A HYBRID GENETIC ALGORITHM FOR THE EARLY/TARDY SCHEDULING PROBLEM

2006 ◽  
Vol 23 (03) ◽  
pp. 393-405 ◽  
Author(s):  
JORGE M. S. VALENTE ◽  
JOSÉ FERNANDO GONÇALVES ◽  
RUI A. F. S. ALVES
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Solution Quality ◽  
New Approach ◽  
Random Keys ◽  
Chromosome Representation ◽  
Local Search Procedure ◽  
Better Than

In this paper, we present a hybrid genetic algorithm for a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys. The genetic algorithm is used to establish the order in which the jobs are initially scheduled, and a local search procedure is subsequently applied to detect possible improvements. The approach is tested on a set of randomly generated problems and compared with existing efficient heuristic procedures based on dispatch rules and local search. The computational results show that this new approach, although requiring slightly longer computational times, is better than the previous algorithms in terms of solution quality.

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