By Anastassia Angelopoulou, Alexandra Psarrou, José García-Rodríguez (auth.), Ignacio Rojas, Gonzalo Joya, Joan Cabestany (eds.)
This two-volume set LNCS 7902 and 7903 constitutes the refereed lawsuits of the twelfth overseas Work-Conference on synthetic Neural Networks, IWANN 2013, held in Puerto de los angeles Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been rigorously reviewed and chosen from quite a few submissions for presentation in volumes. The papers discover sections on mathematical and theoretical equipment in computational intelligence, neurocomputational formulations, studying and edition emulation of cognitive features, bio-inspired platforms and neuro-engineering, complex themes in computational intelligence and applications.
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Extra info for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part II
CIVR 2005. LNCS, vol. 3568, pp. 226–236. hk Abstract. The topological features of the communication network between computing nodes in Parallel Genetic Algorithms, under the framework of the island model, is discussed in the context of both the local rate of information exchange between nodes, and the global exchange rate that measures the level of information flow in the entire network. For optimal performance of parallel genetic algorithm for a set of benchmark functions, the connectivity of the network can be found, corresponding to a global information exchange rate between 40-70%.
It is based upon the systematic change of several neighbourhood structures (Hansen and Mladenovi´c ). In this work we develop two VNSs in order to address the TBAP and the QCSP, respectively. Later, these metaheuristics are integrated into a framework. 1 VNS Algorithm for Solving the TBAP Algorithm 1 depicts the pseudocode of the proposed VNS aimed at solving the TBAP. The VNS uses two neighbourhood structures based upon the reinsertion movement Na (γ, λ), namely, λ vessels and their assigned proﬁles are removed from the berth b ∈ B and reinserted in another berth b , where b = b , and the interchange movement Nb (γ), which consists in exchanging a vessel v ∈ V assigned to berth b ∈ B with another vessel v assigned to berth b , where b = b .
Table 2 shows the topographic product for a number of nodes. We can see that the insertion of more nodes makes no diﬀerence to the object’s topology. Based on the maximum size of the network an optimum result is achieved when at least half of the network is developed. Table 2 shows that for the diﬀerent type of gestures this optimum number is in the range > 90 and < 130. Fig. 4. (a), (b), and (c) Plot of data set for similarity thresholds eT = 100, eT = 500, and eT = 900. (d), (e), and (f) Plot of the MDL(K) values versus the number of clusters centres generated by the similarity thresholds during the growth process of the GNG.
Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part II by Anastassia Angelopoulou, Alexandra Psarrou, José García-Rodríguez (auth.), Ignacio Rojas, Gonzalo Joya, Joan Cabestany (eds.)