scholarly journals Novel Method Using Crossover (Genetic Algorithms) With Matrix Technique to Modifying Ciphering by Using Playfair

2013 ◽  
Vol 6 (3) ◽  
pp. 97-106
Author(s):  
Mohammed Sami Mohammed
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
The Other ◽  
Plain Text ◽  
Cipher Text ◽  
One Step ◽  
Novel Method ◽  
Get Complex ◽  
New Generation

Two techniques combined with each other, to get complex one. One from technique of genetic algorithm (GA) and the other is PlayFair cipher method, in this research using one step of Genetic Algorithm (GA) which called Crossover to make offspring of two parents (characters) to get one or two new character by using these techniques then using PlayFair technique to cipher text (plain text). So the person who wants to break code, two techniques must know.This research is a novel method of ciphering by getting a new generation of offspring from two characters, when we give a new theory of symbols as mention in research.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

scholarly journals NON LINEAR FEEDBACK STREAM CIPHER

2012 ◽  
pp. 132-138
Author(s):  
Dr .R. Siva Ram Prasad ◽  
G. Murali ◽  
S. Gopi Krishna
Keyword(s):  
Linear Function ◽  
Stream Cipher ◽  
Nonlinear Function ◽  
Linear Feedback ◽  
Synchronous Mode ◽  
Plain Text ◽  
Cipher Text ◽  
Security Software ◽  
Non Linear ◽  
New Generation

The main aim of this paper is to develop a new generation and innovative security software for applications. We proposed new stream cipher called NLFS. NLFS means Non-linear feedback stream cipher, which is a fast and secure stream cipher for egovernance applications. This stream cipher uses AES secure non-linear function and AES key generation. NLFS uses primitive polynomial generated S-boxes in byte substitution step. NLFS uses two similar AES round functions and these two proceed parallelly to produce key-stream. Non-linear *function of NLFS has AES nonlinear function steps (add-round key, byte substitution, mix column, shift rows) and it extra includes value-based rotation step. In value based rotation step it rotates each 8-bit word by its first 3-bit (decimal) value.NLFS have two modes basic mode that is synchronous mode and self synchronous mode. In synchronous mode key stream is independent of plain text and cipher text. In selfsynchronous mode key stream generation depending on cipher text. In self synchronous mode generated keystream update first 512-bit buffer and cipher text update the second buffer.

scholarly journals A Novel Method for Obtaining the Signature of Household Consumer Pairs

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6030
Author(s):  
Dadiana-Valeria Căiman ◽  
Toma-Leonida Dragomir
Keyword(s):  
Genetic Algorithms ◽  
Case Studies ◽  
Nonlinear Regression ◽  
Power Level ◽  
Electricity Consumption ◽  
The Other ◽  
Total Current ◽  
Novel Method

The management of electricity consumption by household consumers requires multiple ways of consumer monitoring. One of these is the signature i(v) determined by monitoring the consumer voltage-current trajectory. The paper proposes a novel method for obtaining signatures of 2-multiple consumers, i.e., a pair of consumers connected in parallel. Signatures are obtained from samples of the voltage at the consumers’ terminals and of the total current absorbed by the consumers, measured at a frequency of only 20 Hz. Within the method, signatures are calculated using genetic algorithms (GA) and nonlinear regression, according to a procedure developed by the authors in a previous paper. The management of the data selected for the signature assignment represents the novelty. The method proposed in this paper is applied in two case studies, one concerning household consumers within the same power level, the other for household consumers of different power levels. The results confirm the possibility of obtaining signatures of i(v) type.

Applicability of Cellular Automata in Cryptanalysis

2017 ◽  
Vol 8 (2) ◽  
pp. 38-48 ◽  
Author(s):  
Harsh Bhasin ◽  
Naved Alam
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cellular Automata ◽  
Literature Review ◽  
Search Problem ◽  
Extensive Literature ◽  
Cipher Text ◽  
Soft Computing Techniques ◽  
A New Technique ◽  
The Given

Cryptanalysis refers to finding the plaintext from the given cipher text. The problem reduces to finding the correct key from a set of possible keys, which is basically a search problem. Many researchers have put in a lot of effort to accomplish this task. Most of the efforts used conventional techniques. However, soft computing techniques like Genetic Algorithms are generally good in optimized search, though the applicability of such techniques to cryptanalysis is still a contentious point. This work carries out an extensive literature review of the cryptanalysis techniques, finds the gaps there in, in order to put the proposed technique in the perspective. The work also finds the applicability of Cellular Automata in cryptanalysis. A new technique has been proposed and verified for texts of around 1000 words. Each text is encrypted 10 times and then decrypted using the proposed technique. The work has also been compared with that employing Genetic Algorithm. The experiments carried out prove the veracity of the technique and paves way of Cellular automata in cryptanalysis. The paper also discusses the future scope of the work.

scholarly journals Increasing Complexity of Cryptographic Using Combination of Hill Cipher and Cipher Block Chaining

2020 ◽  
Vol 4 (2) ◽  
pp. 92-97
Author(s):  
Abd Charis Fauzan ◽  
Veradella Yuelisa Mafula
Keyword(s):  
Text Messages ◽  
The Other ◽  
Text Documents ◽  
Plain Text ◽  
Cipher Text ◽  
Hill Cipher ◽  
Mode 3 ◽  
Two Stages ◽  
The Hill ◽  
Block Chaining

Security and confidentiality of documents stored on a computer is an important aspect in the field of computer or information system security. Documents will no longer be useful if they are intercepted or hijacked by unauthorized people, they will even endanger the document owner, if documents containing important information are misused by irresponsible people. Therefore the documents on the computer must be preserved so that they are only accepted and used by interested persons. One of the solutions to prevent eavesdropping of documents is to use cryptography. This study aims to increase cryptographic complexity using a combination of hill cipher algorithms and block chaining cipher modes. The combination of the two methods is expected to be able to cover the shortcomings of each method so that the cryptographic complexity can be increased. The method in this study consists of two stages, namely the document encryption and document decryption stages. The encryption stage is to change the plaintext document into a ciphertext document, on the other hand, the decryption stage is to change the ciphertext document back into a plaintext document. The stages for document encryption include; 1) retrieve plain text messages in the document so that they can be processed by the system (parsing); 2) encryption to convert plaintext documents into ciphertext documents using the hill cipher algorithm; 3) encryption to convert plaintext documents into cipher text documents using cipher block chaining mode. While the methods for decryption are 1) taking the ciphertext message in the document so that it can be processed by the system (parsing); 2) decryption to convert ciphertext documents into plaintext documents using cipher block chaining mode; 3) decryption to convert ciphertext documents into plaintext documents using a hill cipher.

Backcalculation Analysis of Pavement-Layer Moduli Using Genetic Algorithms

1997 ◽  
Vol 1570 (1) ◽  
pp. 134-142 ◽  
Author(s):  
T. F. Fwa ◽  
C. Y. Tan ◽  
W. T. Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Performance Evaluation ◽  
Objective Function ◽  
The Other ◽  
Local Minima ◽  
Structural Composition ◽  
Genetic Algorithm Approach ◽  
Solution Surface ◽  
Surface Deflections

Most existing iterative backcalculation programs for pavement layer moduli arrive at their solutions by minimizing an objective function related to the differences between computed and measured surface deflections. Unfortunately, the solution surface of the backcalculation problem of pavement-layer moduli is known to contain many local minima. A potentially good backcalculation procedure would be one that has a strong global search ability to overcome the problem of local minima. The genetic algorithm (GA) is a technique that satisfies this requirement. The development of a backcalculation program known as NUS-GABACK using the genetic-algorithm approach is presented, along with the formulation and operations of the program. A detailed performance evaluation of the GA-based method is made against four other programs by solving five backcalculation problems with different structural composition. It was found that NUS-GABACK performed comparably well against the other programs and demonstrated consistency in the accuracies of backcalculated moduli.

Mathematical Modeling and Genetic Algorithms for Product Sequencing in a Cellular System

2014 ◽  
pp. 31-47 ◽  
Author(s):  
Gürsel A. Süer ◽  
Fatih Yarimoglu
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
The Other ◽  
Scheduling Problem ◽  
Optimal Result ◽  
Manufacturing Environment ◽  
Reasonable Time ◽  
Sequencing Problem

This chapter considers a product-sequencing problem in a synchronized manufacturing environment, which is using a uniform time bucket approach for synchronization. This problem has been observed in a jewelry manufacturing company and is valid in other labor-intensive cellular environments. The scheduling problem handled has two aspects: first, determining manpower allocation; second, sequencing the products in order to minimize the number of periods where available manpower is exceeded. The number of operators needed in a time bucket may exceed the available manpower level as different products have different manpower requirements for different processes. A mathematical model is developed for the manpower allocation part of the problem. To perform product sequencing, two methods are used, namely mathematical modeling and genetic algorithm. A new five-phase GA approach is proposed, and the results show that it outperforms the classical GA. Several experiments have been conducted to find better GA parameters as well. Finally, GA results are compared with mathematical model results. Mathematical Modeling finds optimal result in a reasonable time for small problems. On the other hand, for the bigger problems, genetic algorithm is a feasible approach to use.

scholarly journals A new culture medium based on genetic algorithms for Isochrysis galbana production relevant to hatcheries

2021 ◽  
Author(s):  
J. Camacho-Rodríguez ◽  
J. J. Gallardo-Rodríguez ◽  
M. C. Cerón-García ◽  
F. García-Camacho ◽  
E. Molina-Grima
Keyword(s):  
Genetic Algorithm ◽  
Fatty Acids ◽  
Genetic Algorithms ◽  
Culture Medium ◽  
Isochrysis Galbana ◽  
Nutrient Content ◽  
The Other ◽  
Commercial Formulation ◽  
Reference Medium ◽  
Seven Generations

AbstractThe nutrient content of a commercial seawater culture medium for growing the microalga Isochrysis galbana was optimized using a stochastic strategy based on genetic algorithms. For this, 210 experiments spread over seven generations were carried out. This strategy reduced the number of assays by more than 90% compared to a factorial design involving the optimization of twelve nutrients simultaneously. The optimized medium outperformed the reference medium in all aspects. The genetic algorithm strategy achieved a polyunsaturated fatty acids (PUFAs) productivity of 7.8 mg L−1 day−1 in a continuous culture of I. galbana, corresponding to an increase of 15% compared to the commercial formulation. Carotenoids, on the other hand, increased by 50% d.w. In addition, PUFA yields were significantly improved, which allowed us to reduce the requirement of several nutrients, for instance, N (25%), Mo (20%), Mn (60%), Co (60%), and Cu (60%).

scholarly journals SIMULATION OF PSO BASED APPROACH FOR CMOL CELL ASSIGNMENT PROBLEM

2015 ◽  
Vol 3 (5) ◽  
pp. 1-12
Author(s):  
Prateek Shrivastava ◽  
Khemraj Deshmukh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Assignment Problem ◽  
Optimization Technique ◽  
Particle Swarm ◽  
The Other ◽  
Swarm Optimization ◽  
Cell Assignment

Particle swarm optimization (PSO) approach is used over genetic algorithms (GAS) to solve many of the same kinds of problems. This optimization technique does not suffer, however, from some of GA’s difficulties; interaction in the group enhances rather than detracts from progress toward the solution. Further, a particle swarm system has memory, which the genetic algorithm does not have. In particle swarm optimization, individuals who fly past optima are tugged to return toward them; knowledge of good solutions is retained by all particles. The genetic algorithm works with the concept of chromosomes having gene where each gene act as a block of one solution. This is totally based on the solution which is followed by crossover and then mutation and finally reaches to fitness. The best fitness will be considered as a result and implemented in the practical area. Due to some drawbacks and problems exist in the genetic algorithm implemented, scientists moved to the other algorithm technique which is apparently based on the flock of birds moving to the target. This effectively overcome the shortcomings of GA and provides better fitness solutions to implement in the circuit.

Optimization of Multistage Vapor Compression Systems Employing Genetic Algorithms

2005 ◽  
Author(s):  
A. C. West ◽  
S. A. Sherif
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Code ◽  
Coefficient Of Performance ◽  
Vapor Compression ◽  
Termination Criteria ◽  
Multistage System ◽  
New Generation ◽  
Survival Of The Fittest ◽  
Vapor Compression Systems

Genetic algorithms involve the coding of a solution into a binary string in the same manner that DNA is a biological coding. A population of binary strings are randomly created, evaluated, allowed to mate, and mutated to form a new generation of strings. There is a mating preference given to those strings which rate the highest to simulate the survival of the fittest theory that exists in nature. This process of evaluation, mating, and mutation is repeated until some termination criteria are met. A computer code was written to simulate the vapor compression systems and perpetuate the genetic algorithm. The genetic algorithm functioned adequately enough to provide general trends but it did not find a universal optimum. After numerous runs, the code produced data that suggest that systems which employ intercooler/flash tanks and operate at lower evaporating temperatures have a higher multistage effectiveness. Multistage effectiveness is a novel term defined as the ratio of the overall coefficient of performance (COP) of the multistage system and the combined coefficient of performance of a group of basic vapor compression systems with cooling capacities and evaporating temperatures that parallel the evaporators in the multistage system.

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