The Applicability of Genetic algorithm to Vertex Cover

Abstract

The Vertex Cover Problem calls for the selection of a set of vertices(V) in a way that all the edges of the graph, connected to those vertices constitute the set E of the given graph G= (V, E). The problem finds applications in various fields and is therefore, one of the most widely researched topics in NP Complete Problems. The problem is an NP Complete problem this work proposes a Genetic Algorithm based solution to handle the problem. The proposed algorithm has been implemented and tested for various graphs. These instances vary in the number of vertices and connectivity. The results are encouraging. This paper also explores the available techniques in order to put the things in the perspective. The future scope of this work intends to apply Diploid Genetic Algorithms to the problem to incorporate robustness into the proposed algorithm.

Cryptanalysis of Stream Cipher Cryptosystem Based on Soft Computing Techniques

“…The following Tables (3,4,5) shows the results of applying proposed cryptanalysis system GA and PSO For Popsize (20,100,200) and Maxiter(10,300) For TxtLen=100,40,10 characters ,the following notations are used: Popsize = Population size MaxIter= Maximum Iteration BF=Best Fitness T/sec=Time/second T.T/sec=Total Time/second Iter_Num= Iteration_Number Fig. (1) shows the comparison between GA and PSO in cryptanalysis system for Popsize (20) and MaxIter(100) for TxtLen=100…”

“…It is a method of transforming cipher text into a plaintext without knowing the key or algorithm [1] [2].However the cryptanalysis of stream ciphers through soft computing techniques as Particle Swarm Optimization (PSOs), Genetic Algorithms (GAs)is still an emerging issue.GA is based on the evolutionary ideas of Natural selection and genetics [3].GAis a good condidate for the optimal solutions to optimization and search problems. The algorithm have been successfully applied to Vertex-Cover problem [4] [5], Maximum-Clique problem [6] [7], Regression testing [8], N-puzzle problem [9], Traveling Salesman Problem [10]. PSO was originally developed by a social-psychologist J. Kennedy and an electrical engineer R.Eberhart in 1995 and emerged from earlier experiments with algorithms that modeled the "flocking behavior" seen in many species of birds.…”

The Applicability of Genetic Algorithm in Cryptanalysis: A Survey

“…GAs have many application [4][5][6][7][8][9][10][2].The work provide a systematic review in a order to understand the scope of GAs in a field of Cryptanalysis. Application of GAs, for cryptanalysis of asymmetric cipher is a field that needs to be explored.…”

“…The algorithm are known for the useful solutions to optimization and search problems [48] [4][5][6][7][8][9][10]. The algorithm have been successfully applied to Vertex-Cover problem [4] [5], Maximum-Clique problem [6] [7], Regression testing [8], N-puzzle problem [9], Traveling Salesman Problem [10].…”

Most Valuable Player Algorithm for Solving Minimum Vertex Cover Problem

“…Examples of heuristic algorithms are A* heuristic search algorithm and iterative deepening A* (IDA*) heuristic search algorithm [5]. Examples of metaheuristic algorithms are sea lion optimization, humpback whale optimization, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Chemical Reaction Optimization (CRO) [6][7][8][9][10][11][12][13][14].…”