scholarly journals An Improved Genetic Algorithm for Developing Deterministic OTP Key Generator

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ashish Jain ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Experimental Work ◽  
Computer Hardware ◽  
Future Research ◽  
Key Generation ◽  
Improved Genetic Algorithm ◽  
Linear Congruential Generator ◽  
The One ◽  
Linear Congruential

Recently, a genetic-based random key generator (GRKG) for the one-time pad (OTP) cryptosystem has been proposed in the literature which has certain limitations. In this paper, two main characteristics (speed and randomness) of the GRKG method are significantly improved by presenting the IGRKG method (improved genetic-based random key generator method). The proposed IGRKG method generates an initial pad by using linear congruential generator (LCG) and improves the randomness of the initial pad using genetic algorithm. There are three reasons behind the use of LCG: it is easy to implement, it can run efficiently on computer hardware, and it has good statistical properties. The experimental results show the superiority of the IGRKG over GRKG in terms of speed and randomness. Hereby we would like to mention that no prior experimental work has been presented in the literature which is directly related to the OTP key generation using evolutionary algorithms. Therefore, this work can be considered as a guideline for future research.

An efficient intelligent decision method for bionic motion unmanned system

2021 ◽  
pp. 095965182110655
Author(s):  
Haipeng Chen ◽  
Wenxing Fu ◽  
Yuze Feng ◽  
Jia Long ◽  
Kang Chen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Motion Control ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Decision Method ◽  
Multiple Traveling Salesman Problem ◽  
Intelligent Decision ◽  
Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

scholarly journals Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1472 ◽  
Author(s):  
Manuel Guerrero ◽  
Raul Baños ◽  
Consolación Gil ◽  
Francisco G. Montoya ◽  
Alfredo Alcayde
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Power Systems ◽  
Community Detection ◽  
North American ◽  
Flow Analysis ◽  
Power Grids ◽  
Improved Genetic Algorithm ◽  
Continental Scale ◽  
The North

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).

A New Binocular Camera Calibration Method Based on an Improved Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 602-608 ◽  
Author(s):  
Xiang Kui Jiang
Keyword(s):  
Genetic Algorithm ◽  
Camera Calibration ◽  
Calibration Method ◽  
Improved Genetic Algorithm ◽  
Linear Calibration ◽  
Calibration Methods ◽  
Calibration Accuracy ◽  
Encoding Method ◽  
The One ◽  
Crossover And Mutation

In this paper,an improved genetic algorithm was proposed,which is applicable to binocular camera calibration. On the one hand, conventional encoding method is improved so that variable search interval can be adjusted adaptively. On the other hand, crossover and mutation probability is varied by using superiority inheritance principle to avoid premature question. Experimental results show that the proposed method has a higher calibration accuracy and better robustness, compared to those of non-linear calibration methods. The proposed method is able to improve the performance of global optimization effectively.

scholarly journals The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

2020 ◽  
Vol 27 (4) ◽  
pp. 488-508
Author(s):  
Anton Olegovich Bassin ◽  
Maxim Viktorovich Buzdalov ◽  
Anatoly Abramovich Shalyto
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Negative Impact ◽  
The Self ◽  
Adjustment Procedure ◽  
Random Weights ◽  
The One ◽  
Adjustment Of Parameters ◽  
Original Rule

Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ,λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation.We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear function with random weights, as well as on random satisfiable MAX-3SAT problems.

Ship Cabin Layout Optimization Design Based on the Improved Genetic Algorithm Method

2013 ◽  
Vol 300-301 ◽  
pp. 146-149 ◽  
Author(s):  
Yun Long Wang ◽  
Chen Wang ◽  
Yan Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
The Other ◽  
Improved Genetic Algorithm ◽  
Local Optimal Solution ◽  
Genetic Algorithm Method ◽  
Np Hard Problem ◽  
The One

Based on the improved genetic algorithm method, a kind of the optimization techniques to solve the problem about the ship cabin layout is presented. The problem about the ship cabin layout is a NP-hard problem. This article has used the genetic algorithm method to solve it .However, for the simple and easy procedure, the basic genetic algorithm is slow and easy to fall into a local optimal solution. Therefore, it must be improved. This article has made the following two improvements: on the one hand using the niche method to solve the multi- peak problem; on the other hand using the climbing method to solve the slow and premature convergence. The simulation tests show that this approach proposed by authors is feasible and valid and the result is satisfied.

scholarly journals Improved Genetic Algorithm Based Classification

2012 ◽  
pp. 211-217
Author(s):  
Keshavamurthy B. N ◽  
Asad Mohammed Khan ◽  
Durga Toshniwal
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Probabilistic Models ◽  
Fitness Function ◽  
Optimal Solution ◽  
Search Space ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Useful Knowledge ◽  
Bayes Algorithm

Classification is the supervised learning technique of data mining which is used to extract hidden useful knowledge over a large volume of databases by predicting the class values based on the predicting attribute values. Of the various techniques, the most widely talked ones include decision tree, probabilistic model and evolutionary algorithms. Recently, the probabilistic and evolutionary techniques are most worked upon, because of the fact that probabilistic models yields high accuracy when there is no attribute dependency in the existing problem and evolutionary algorithms are used to obtain optimal solution over a large search space very quickly when there is less information known about a problem and problem posses attribute dependency. Though there are tradeoffs in each model still there are scopes to improve upon the existing. The proposed approach improves the evolutionary technique such as genetic algorithm by improving the fitness function parameters. Also, in this we compare the genetic algorithm results with Naïve Bayes algorithm results. For the experimental work we have used the nursery data set taken from the UCI Machine Learning Repository.

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