Abstract
<p class="0abstract">A Genetic algorithm is a search algorithm depends on the methodology of natural selection and natural genetics. A Mobile Ad hoc network (MANET) is a type of wireless nodes (Devices) which are free to move anywhere in the network without any constraints. The nodes which are in range can communicate each other through radio waves and those who are not in range use any routing algorithm for communication. In this review paper, we focus on the problems of MANET that has been solved by applying GA for it and highlights the characteristic and challenges of MANET in the literature. More specifically, we present the summary of review papers and basic solutions that use and in the last, we present some future direction. Consequently, we concluded that modification in Fitness function (Evaluation function) according to the problem is the base of Genetic algorithm and variation in algorithm parameters can give solutions in a reasonable time.</p>
Highlights
The core behind communication between electronic devices using wireless network without any fixed infrastructure has gained active attention of researchers since its reign
(C RAJAN 2015) this paper presents a combination of two algorithms Hybrid Optimization Algorithms (Genetic and Particle swarm optimization) for Multicasting Routing in Mobile Ad hoc network (MANET).A MANET network has been taken as a graph where one variable represents (Set of nodes) and the other represents (Set of links)
We have presented a summary and simulation result of different paper that use genetic algorithms (GAs) to overcome the problems of the MANET
Summary
The core behind communication between electronic devices using wireless network without any fixed infrastructure has gained active attention of researchers since its reign. Mobile Ad-hoc Networks (MANETs) are extraordinary sort of system in which the portability of the nodes is very high. Modern computers and laptops based on multicore architectures are capable of running millions of operations per second, making the application of evolutionary algorithms a reality in many scientific areas They are based on the evolution of a population of potential solutions by the mean of genetic operators like crossover and mutation [2].GA is an evolutionary optimization approach, they are applicable to problems which are large, non-linear and possibly discrete in nature [4]. A Second section of the paper includes a literature review about MANET evolution, characteristic and application and evolutionary algorithm (Genetic algorithm).
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