By Anna I. Esparcia-Alcázar, Antonio M. Mora
This ebook constitutes the completely refereed post-conference complaints of the overseas convention at the purposes of Evolutionary Computation, EvoApplications 2014, held in Granada, Spain, in April 2014, colocated with the Evo* 2014 occasions EuroGP, EvoCOP, and EvoMUSART.
The seventy nine revised complete papers awarded have been rigorously reviewed and chosen from 128 submissions. EvoApplications 2014 consisted of the subsequent thirteen tracks: EvoCOMNET (nature-inspired concepts for telecommunication networks and different parallel and allotted systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in strength applications), EvoFIN (evolutionary and ordinary computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in snapshot research, sign processing, and development recognition), EvoINDUSTRY (nature-inspired innovations in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for danger administration, protection and defence applications), EvoROBOT (evolutionary computation in robotics), EvoSTOC (evolutionary algorithms in stochastic and dynamic environments), and EvoBio (EC and comparable strategies in bioinformatics and computational biology).
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Additional resources for Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers
The neighbors who have information about the peer requested send their recommendations. These experiences about another peer are called recommendations. A recommendation contains the following information: average number of successful interactions, average satisfaction of interactions, average weight of interactions, and calculated trust value of the queried peer. The recommendation based features are listed in Table 2: Table 2. Recommendation Based Features Feature Symbol number of recommendations f7 average of neighbours’ average number of successful interactions f8 average of neighbours’ average satisfaction values f9 average of neighbours’ average weight values f10 average of trust values f11 In our genetic model, we use simple operators to generate a formula for trust calculation.
If a peer uploads a virus infected or inauthentic ﬁle, it is counted as a ﬁle-based attack. E. Tahta et al. Table 3. 1 the number of attacks when a trust model does not exist. Then the simulations are run with the evolved trust model. Success of the trust model is assessed by the number of attacks prevented with the model. Table 3 shows the success ratio of the evolved trust model against individual attackers according to varying malicious peer populations in the network. The model has a notable success against individual naive attackers.
Participants in eBay can rate each other at the end of auctions and information about auctions is stored in the central server. However, having a central authority conﬂicts with the nature of P2P systems. Thus peers need to organize themselves to manage and store information about their trust relationships [1–3]. In pure P2P networks like Gnutella , peers ﬂood trust queries to the network in order to obtain trust information about others. In such a network, all peers store trust information about neighbors according to the past interactions [2,5,6].
Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers by Anna I. Esparcia-Alcázar, Antonio M. Mora