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 foreign 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 conscientiously reviewed and chosen from a variety of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical tools in computational intelligence, neurocomputational formulations, studying and version emulation of cognitive features, bio-inspired platforms and neuro-engineering, complex themes in computational intelligence and applications.
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Extra resources 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
CS can be splitted oﬀ in four conceptually independent parts: a) the search metaheuristic (SM), b) the iterative clustering (IC) component, c) the analyzer module (AM), and d) the local searcher (LS). The SM component can be implemented by any optimization algorithm that generates diversiﬁed solutions of the search space. It must work as a full-time solution generator, exploring the search space by manipulating a set of solutions, according to its speciﬁc search strategy. IC component aims to gather similar solutions into groups, maintaining a representative cluster center for them.
HABC got bad performance at Schwef el7 , because this function is extremely multimodal and HABC performs many calls to Hooke & Jeeves, without information about which food sources are promissing, getting parked in local minimum points. 5 was used because is the best one to overall functions. S. M. de Oliveira Fig. 1. Convergence history comparison of all algorithms at Langerman Table 4. 866 Figure 1 illustrates the progress of all algorithms minimizing Langerman function. The mean of objective function (known as ﬁtness value in the chart) was calculated between 30 trials at intervals of 5000 function evaluations.
ABCS is described in section 3 and computational results are presented in 4. The ﬁndings and conclusions are summarized in section 5. 2 Algorithms Foundations A challenge in hybrid metaheuristic is to employ eﬃcient strategies to cover all the search space, applying local search only in actually promising search areas. The inspiration in nature has been pursued to design ﬂexible, coherent and eﬃcient computational models. In this section, the Clustering Search (*CS) and ABC are brieﬂy described, introducing concepts needed to further explain how to combine them to allow detecting promising search areas before applying the Hooke & Jeeves local search procedure .
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.)