scholarly journals Applicability of Cellular Automata in Cryptanalysis

Cryptography ◽  
2020 ◽  
pp. 180-191
Author(s):  
Harsh Bhasin ◽  
Naved Alam
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Cellular Automata ◽  
Literature Review ◽  
Soft Computing ◽  
Search Problem ◽  
Extensive Literature ◽  
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.

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.

Gene Expression Programming

2017 ◽  
pp. 269-292 ◽  
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

Where Genetic Algorithms Excel

2001 ◽  
Vol 9 (1) ◽  
pp. 93-124 ◽  
Author(s):  
Eric B. Baum ◽  
Dan Boneh ◽  
Charles Garrett
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selective Breeding ◽  
Population Distribution ◽  
Fitness Function ◽  
Mean Field ◽  
Search Problem ◽  
Parallel Search ◽  
Type Algorithm ◽  
Insight Into

We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve “implicit parallelism” in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.

scholarly journals Automatic Procedures as Help for Optimal Cam Design

2006 ◽  
Author(s):  
Massimo Antonini ◽  
Alberto Borboni ◽  
Roberto Bussola ◽  
Rodolfo Faglia
Keyword(s):  
Genetic Algorithm ◽  
Soft Computing ◽  
Design Process ◽  
Model Identification ◽  
Optimization Method ◽  
Realistic Model ◽  
Design Problems ◽  
Soft Computing Techniques ◽  
Cam Follower ◽  
Set Up

In this work we suggest a synthesis of recent results obtained on the application of soft-computing techniques to solve typical automatic machines design problems. Particularly, here we show an optimization method based on the application of a specialized algorithms ruled by a generalized software procedures, which appears able to help the mechanical designer in the first part of the design process, when he has to choose among different wide classes of solutions. In this frame, among the different problems studied, we refer here about the choice of the best class of motion profiles, to be imposed to a cam follower, which must satisfy prefixed design specifications. A realistic behaviour of the system is considered and the parameter model identification is set up by a soft computing procedure. The design, based on theoretical knowledge, sometimes is not sufficient to fulfil desired dynamical performances, in this situation, a residual optimization is achieved with the help of another optimizing method. The problem of a cam-follower design is presented. A class of motion profiles and the best theoretical motion profile is selected by an evolutionary algorithm. A realistic model is considered and its parameter identification is achieved by a genetic algorithm. The residual optimization is achieved by a servomotor optimized by another genetic algorithm. Evolutionary approach is used during all the design process and, as was shown, it allows really interesting performance in terms of simplicity of the design process and in terms of performance of the product.

scholarly journals Hybrid Modeling KMeans – Genetic Algorithms in the Health Care Data

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Tessy Badriyah
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Health Care ◽  
Genetic Algorithms ◽  
Hybrid Method ◽  
Health Care Data ◽  
Computational Performance ◽  
Genetic Algorithm Approach ◽  
A New Technique ◽  
Published Research

K-Means is one of the major algorithms widely used in clustering due to its good computational performance. However, K-Means is very sensitive to the initially selected points which randomly selected, and therefore it does not always generate optimum solutions. Genetic algorithm approach can be applied to solve this problem. In this research we examine the potential of applying hybrid GA- KMeans with focus on the area of health care data. We proposed a new technique using hybrid method combining KMeans Clustering and Genetic Algorithms, called the “Hybrid K-Means Genetic Algorithms” (HKGA). HKGA combines the power of Genetic Algorithms and the efficiency of K-Means Clustering. We compare our results with other conventional algorithms and also with other published research as well. Our results demonstrate that the HKGA achieves very good results and in some cases superior to other methods.Keywords: Machine Learning, K-Means, Genetic Algorithms, Hybrid KMeans Genetic Algorithm (HGKA).

scholarly journals Performance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem

2019 ◽  
Vol 8 (2S4) ◽  
pp. 91-95
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Soft Computing ◽  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Ant Colony ◽  
Travelling Salesman ◽  
Swarm Optimization ◽  
Soft Computing Techniques

The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.

Gene Expression Programming

2021 ◽  
pp. 427-446
Author(s):  
Baddrud Zaman Laskar ◽  
Swanirbhar Majumder
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Natural Selection ◽  
Genetic Programming ◽  
Soft Computing ◽  
Gene Expression Programming ◽  
Research Work ◽  
Genetic Operators ◽  
A New Technique ◽  
Work Done

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.

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