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Download Bio-Inspired Models of Network, Information, and Computing by Gianni A. Di Caro, Guy Theraulaz (eds.) PDF

By Gianni A. Di Caro, Guy Theraulaz (eds.)

This ebook constitutes the completely refereed post-conference complaints of the seventh foreign convention on Bio-Inspired versions of community, info and Computing platforms (Bionetics 2012), held in Lugano, Switzerland, in December 2012. The 23 revised complete papers offered have been rigorously reviewed and chosen from forty submissions. They conceal issues akin to networking, robotics and neural networks, molecular scale and bioinformatics, optimization and bio-inspired modeling in a variety of fields.

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Read or Download Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers PDF

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Additional resources for Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers

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In the second stage, we use the Expectation Maximization (EM) algorithm to estimate the parameters of the mixture model. The EM algorithm provides an efficient iterative procedure to compute the Maximum Likelihood (ML) estimates in the presence of missing or unobservable data. It iterates two steps: in the expectation step (E-step) the distribution of the unobservable variable is estimated and in the maximization step (M-step) the parameters which maximize the expected log likelihood found on the E-step are calculated.

Basing on the Swarm Intelligence paradigm and its derivative, the Ant Colony Optimization scheme [11], there were various ant routing algorithms proposed for both fixed and wireless telecommunication networks. The first ant routing algorithm designed for asymmetric packet-switched networks was introduced by Dorigo and di Caro [9]. Their AntNet algorithm implicitly achieves load balancing by employing a probabilistic distribution of packets on multiple paths. The experiments reported in [7–9] proved that AntNet outperforms other competitors, such as Q-routing [4], PQrouting [5], Shortest Path First (SPF) and OSPF [12], in terms of the throughput and the delay.

9, 286–294 (1982) 14. The network simulator ns-2. pl NASK Research and Academic Computer Network, 02-796 Warsaw, Poland Abstract. This paper introduces the Reactive ASR-FA algorithm which is a novel ant routing algorithm that utilizes statistical models of packet delay to detect changes in the network conditions. The algorithm is able to quickly react to various load level changes by temporarily modifying the learning parameter’s settings. We show in a set of experiments that using the Reactive ASR-FA significantly speeds up the adaptation process of ant routing algorithms and assures lower values of the mean packet delay.

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