Download PDF by Fatma Corut Ergin, A. Şima Uyar, Ayşegül Yayimli (auth.),: Applications of Evolutionary Computation: EvoApplications

By Fatma Corut Ergin, A. Şima Uyar, Ayşegül Yayimli (auth.), Cecilia Di Chio, Anthony Brabazon, Gianni A. Di Caro, Rolf Drechsler, Muddassar Farooq, Jörn Grahl, Gary Greenfield, Christian Prins, Juan Romero, Giovanni Squillero, Ernesto Tarantino, Andrea G. B

ISBN-10: 3642205194

ISBN-13: 9783642205194

ISBN-10: 3642205208

ISBN-13: 9783642205200

This booklet constitutes the refereed complaints of the overseas convention at the purposes of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 occasions. due to the big variety of submissions acquired, the complaints for EvoApplications 2011 are divided throughout volumes (LNCS 6624 and 6625). the current quantity comprises contributions for EvoCOMNET, EvoFIN, EvoIHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOC. The fifty one revised complete papers offered have been rigorously reviewed and chosen from quite a few submissions. This quantity provides an outline concerning the most up-to-date study in EC. components the place evolutionary computation suggestions were utilized diversity from telecommunication networks to complicated structures, finance and economics, video games, photo research, evolutionary song and paintings, parameter optimization, scheduling, and logistics. those papers could provide instructions to assist new researchers tackling their very own challenge utilizing EC.

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P|B| ). e. pfb ∈ P = {P1 , . . , P|P| }. We remark that establishing the power emission pfb ∀ b ∈ B, f ∈ F does not completely characterize a solution of the PFMAP. We indeed have to fix the value of the assignment variables xftbh and thus we need to set some assignment rule. |F | First, note that given a power vector p = (p11 , p21 , . . , p|B| ) and a receiver t ∈ T we can compute the power Pbf (t) that t receives from b on f , ∀b ∈ B, f ∈ F . Through Pbf (t), if we fix the server β ∈ B of t, we can check if there exists a SIR inequality (2) that is satisfied for some frequency f ∈ F and burst profile h ∈ H.

These stable single-route solutions correspond to paths that have the smallest hop count. In this paper, we leverage this idea to improve the performance of ant-based routing protocols by dynamically adjusting the routing exponent. The results are validated via simulation. 1 Introduction Swarm intelligence is a term that refers to the action of a locally coordinated group of individuals that can achieve a complex objective or behavior. Often the local coordination algorithms are inspired by ecological systems including social insects, self-organizing colonies of single-celled organisms or movements of larger animals such as flocks of birds.

If the network consists of m nodes, we define y(n) be the m-dimensional vector probability density of ants over the network at the nth time step. The forward ants traverse the network following the Markov process according to a transition matrix P (n) (β) = [pji ] at the nth time step, y(n+1) = P (n) (β)y(n) (2) because both probability density and pheromone values on each link are evolving only depend on present state by every discrete synchronous step. Here the k th component of the density vector y(n) is the probability of finding an ant on the k th node of the network.

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Applications of Evolutionary Computation: EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27-29, 2011, Proceedings, Part II by Fatma Corut Ergin, A. Şima Uyar, Ayşegül Yayimli (auth.), Cecilia Di Chio, Anthony Brabazon, Gianni A. Di Caro, Rolf Drechsler, Muddassar Farooq, Jörn Grahl, Gary Greenfield, Christian Prins, Juan Romero, Giovanni Squillero, Ernesto Tarantino, Andrea G. B


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