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 court cases of the twelfth overseas Work-Conference on synthetic Neural Networks, IWANN 2013, held in Puerto de l. a. Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been rigorously reviewed and chosen from a number of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical equipment in computational intelligence, neurocomputational formulations, studying and version emulation of cognitive capabilities, bio-inspired platforms and neuro-engineering, complex themes in computational intelligence and applications.
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Additional 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
In spite of the great deal of attention gathered by these operational problems over the last years (Steenken et. al ), there is a lack of integration approaches in which the interactions between them are considered. In this work we address 30 E. Lalla Ruiz et al. the integration of the seaside operations in a maritime container terminal. In this context, we propose two VNS algorithms in order to individually tackle the TBAP and the QCSP, respectively. These metaheuristics are integrated into an eﬀective framework with the goal of providing an overall planning for serving incoming container vessels within a given time horizon.
Faster variants get worse topology preservation but the network converges quickly. However, the representation is suﬃcient and can be used in situations where minimum time is required like online learning for detecting obstacles in robotics where you can obtain a rough representation of the object of interest in a given time and with minimum quality. Table 1. 0111 Model Probability in Self-organising Maps 7 Figure 4 shows the plots of the MDL(K) values versus the number of clusters for minimum, maximum and approximate match similarity threshold (eT = 100, eT = 900, and eT = 500).
Adaptive Genetic Algorithm and Quasi-parallel Genetic Algorithm: Application to Knapsack Problem. , Waśniewski, J. ) LSSC 2005. LNCS, vol. 3743, pp. 189–196. Springer, Heidelberg (2006) 18. : Locus Oriented Adaptive Genetic Algorithm: Application to the Zero/One Knapsack Problem. In: RASC 2004, pp. 410–415 (2004) 19. : Adaptive Genetic Algorithm with Mutation and Crossover Matrices. In: IJCAI 2007, pp. 2330–2333 (2007) 20. : Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on RealParameter Optimization (2005) 21.