As the applications of systems are increasing in various aspects of our daily life, it enhances the complexity of systems in Software design (Program response according to environment) and hardware components (caches, branch predicting pipelines). Within the past couple of years the Test Engineers have developed a new testing procedure for testing the correctness of systems: namely the evolutionary test. The test is interpreted as a problem of optimization, and employs evolutionary computation to find the test data with extreme execution times. Evolutionary testing denotes the use of evolutionary algorithms, e.g., Genetic Algorithms (GAs), to support various test automation tasks. Since evolutionary algorithms are heuristics, their performance and output efficiency can vary across multiple runs, there is strong need a environment that can be handle these complexities, Now a day's MATLAB is widely used for this purpose. This paper explore potential power of Genetic Algorithm for optimization by using new MATLAB based implementation of Rastrigin's function, throughout the paper we use this function as optimization problem to explain some key definitions of genetic transformation like selection crossover and mutation.General Terms: Software testing, Evolutionary algorithm.
Financial services have a ubiquitous need however the urban rich have easy and universal access with wider options, compared to the low-income group who are forced to accept informal, expensive and riskier means to fulfill their financial needs. The demand and supply of financial services for the poor is imbalanced, with supply being acutely constrained by lack of viability and sustainability of current business models. Technology and IT has a pivotal role in making financial inclusion a viable reality. Technology, including information technology can enable lowering costs by increasing automation, enhancing efficiency, enabling scaling up through uniformity, consistency and security. Multiple technology choices are available to financial service providers but few have been proven yet. This paper examines technology options at the front end and back-end in detail with a critique of alternatives available for financial inclusion in Indian context.
Financial services have a ubiquitous need however the urban rich have easy and universal access with wider options, compared to the low-income group who are forced to accept informal, expensive and riskier means to fulfill their financial needs. The demand and supply of financial services for the poor is imbalanced, with supply being acutely constrained by lack of viability and sustainability of current business models. Technology and IT has a pivotal role in making financial inclusion a viable reality. Technology, including information technology can enable lowering costs by increasing automation, enhancing efficiency, enabling scaling up through uniformity, consistency and security. Multiple technology choices are available to financial service providers but few have been proven yet. This chapter is based on available front end and back end technology options for financial inclusion. Further, it describe the role of front end and back end technology options in Indian context.
With the exponential growth of networked system and application such as Commerce, the demand for effective internet security is increasing. Cryptology is the science and study of systems for secret communication. It consists of two complementary fields of study: cryptography and cryptanalysis. In this paper, we propose a cryptanalysis method based on Genetic Algorithm and Tabu Search to break a Mono-Alphabetic Substitution Cipher. We will also compare and analyze the performance of these algorithms in automated attacks on a Mono-Alphabetic Substitution Cipher. The use of Tabu Search is a largely unexplored area in the field of Cryptanalysis. A generalized version of these algorithms can be used for attacking other cipher as well.
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