Cryptography is a basic tool for protection and securing data. Security provides safety, reliability and accuracy. Genetic Algorithm (GA) is typically used to obtain solution for optimization and search problems. This paper presents application of GA in the field of cryptography. The selection of key in the field ofpublic key cryptography is a selection process in which keys can be categorized on the basis of their fitness function, making GA a better candidate for the key generation. We propose a new approach for e-security applications using the concept of genetic algorithms with pseudorandom sequence to encrypt and decrypt data stream. Many different image encryption methods have been proposed to keep the security of these images. Image encryption algorithms try to convert an image to another image that is hard to understand.

In this paper the research work has done comparative analysis of one of the famous NP hard problem: NQueen using traditional Backtraking and Genetic Algorithm(GA). The research work has implemented the solution of the NQueen problem using backtracking and using GA. Both the methods of solving NQueen problem are entirely different. The first one is general method and takes time in days , months and years as N increases. In this paper the time taken by the two methods for a given value of N are compared. The work has restricted values of N upto 50 only as beyond this it is extremely difficult to get the solution of the problem using backtracking method. Both the implementation have been carried out in MATLAB using Pentium Core 2 Duo T6600 Core 2.2 GHz processor on Windows 8 with 4GB RAM. Because of the random nature of GA instead of time taken in obtaining all possible solutions for a given value of N, the time taken in obtaining say count number of solutions was first determined using GA, then time taken in same number of solutions that is count using backtracking was noted. Results are then presented in tabular manner.

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In this paper we solve the non fractional knapsack problem also known as 0-1 knapsack using genetic algorithm. The usual approaches are greedy method and dynamic programming. Its an optimization problem where we try to maximize the values that can be put into a knapsack under the constraint of its weight. We solve the problem using genetic algorithm in matlab using gatool. In this research work different selection schemes have been used like roulette wheel, tournament selection, Stochastic selection etc. Following the introduction of genetic algorithm and knapsack problem, formulation of 0-1 knapsack problem in genetic algorithm is presented. Experimental results using various selection schemes have been analyzed and comparison of genetic algorithm technique is done with greedy method and dynamic programming optimizing techniques.

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