Evolutionary Algorithms for Mobile Ad Hoc Networks by Bernabé Dorronsoro

By Bernabé Dorronsoro

This finished consultant describes how evolutionary algorithms (EA) can be used to spot, version, and optimize day by day difficulties that come up for researchers in optimization and cellular networking. It presents effective and exact info on dissemination algorithms, topology administration, and mobility types to handle demanding situations within the box. it's a terrific e-book for researchers and scholars within the box of Read more...

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This finished consultant describes how evolutionary algorithms (EA) can be used to spot, version, and optimize day by day difficulties that come up for researchers in optimization and cellular networking. Read more...

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Instead of evolving a population of similar individuals representing a global solution as in classical EAs, CEAs consider the coevolution of subpopulations of individuals representing different species. Each subpopulation typically runs a genetic algorithm. The specificity of coevolution comes from the fact that the fitness of an individual is dependent on its interaction with other individual(s). These interactions can be either positive or negative according to their influence on the population.

38. J. G. -C. Hu, S. PalChaudhuri, A. K. Saha, and D. B. Johnson. Design and evaluation of a metropolitan area multitier wireless ad hoc network architecture. In Proceedings of the IEEE Workshop on Mobile Computing Systems and Applications, pp. 32–43, 2003. 39. E. E. Johnson, T. Zibin, M. Balakrishnan, Z. Huiyan, and S. Sreepuram. Routing in HF ad-hoc WANs. In Proceedings of the IEEE Military Communications Conference (MILCOM), Vol. 2, pp. 1040–1046, 2004. 40. D. B. Johnsort. Routing in ad hoc networks of mobile hosts.

Negative interactions mean that the success of one species implies the failure of other species; this is competitive coevolution. Positive interactions mean that the success of one species is conditioned to the success of other species; this is cooperative coevolution. In this book only the cooperative model will be considered and more precisely the CCEA from Potter and De Jong [26], which is the most prominent one. Since its introduction in 1994, this cooperative coevolutionary framework has been used to solve many problems, for example, learning [27, 28], function optimization [25], and real-world optimization [15] problems.

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