Routing Genetic Algorithm Analysis

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computer networks, is finding the shortest path (SP) between source and destination. There are different types of networks those networks use multiple Quality of Services (QoS) constraints to find the feasible solution. Genetic Algorithm is one of the soft computing techniques which give desired results for finding the optimal path from source to destination.

Key Words: Routing, Genetic algorithm, QoS
In any kind of networking most important factor affecting network performance is routing. How routing is carried out between source to destination is explained in 1.1. And genetic algorithm is applied to improve the routing and it helps to find the highly optimal (shortest) path from the entire feasible path which is explained
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Genetic algorithm is potentially huge global search algorithm which is used to solve complex issues by natural selection and natural reproduction of biology. Genetic algorithm is an optimization process that represents the process of natural selection. This process is mainly used to develop efficient solutions [1].
Genetic algorithm is a search technique used in computing to find true and approximate solutions to optimization and search problems. A genetic algorithm maintains a population of candidate solutions. Each candidate solution is called a chromosome. Each individual section of the chromosome is called as gene (or) each character in the string, is called a gene. Genetic algorithms are example of evolutionary computing methods and optimization type algorithms. Genetic algorithm is one of the evolutionary techniques done in biology; this evolution produces the best fittest individuals. A set of chromosomes form a population which is evaluated by a fitness function. A genetic algorithm (GA) is a computational representation it first creates the initial population. Candidates of initial population are subjected to calculate the fittest values. After finding the fittest values, which chromosomes have the highest fittest values those chromosomes have to be put into the reproduction operations. Reproduction operations include
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The GA simulates this process through coding and special operators. A genetic algorithm maintains a population of candidate solutions, where each candidate solution is usually coded as binary string called a chromosome. The best choice of coding has been shown to be a binary coding. A set of chromosomes forms a population, which is evaluated and ranked by fitness evaluation function. The fitness evaluation function play a critical role in GAs because it provides information how good each candidate. The initial population is usually generated at random. The evolution from one generation to the next one involves mainly three steps: fitness evaluation, selection and
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