By Juan Julian Merelo, Agostinho Rosa, José M. Cadenas, António Dourado, Kurosh Madani, Joaquim Filipe
The current ebook contains a set of chosen prolonged papers from the 6th foreign Joint convention on Computational Intelligence (IJCCI 2014), held in Rome, Italy, from 22 to 24 October 2014. The convention was once composed by means of 3 co-located meetings: The overseas convention on Evolutionary Computation conception and functions (ECTA), the foreign convention on Fuzzy Computation thought and purposes (FCTA), and the foreign convention on Neural Computation idea and purposes (NCTA). contemporary progresses in medical advancements and functions in those 3 parts are stated during this ebook. IJCCI obtained 210 submissions, from fifty one nations, in all continents. After a double blind paper overview played by means of this system Committee, 15% have been permitted as complete papers and hence chosen for oral presentation. extra papers have been permitted as brief papers and posters. yet another choice used to be made after the convention, dependent additionally at the review of presentation caliber and viewers curiosity, in order that this publication comprises the prolonged and revised models of some of the best papers of IJCCI 2014. dedication to top of the range criteria is an important situation of IJCCI that might be maintained within the subsequent variations, contemplating not just the stringent paper attractiveness ratios but additionally the standard of this system committee, keynote lectures, participation point and logistics.
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Extra resources for Computational Intelligence: International Joint Conference, IJCCI 2014 Rome, Italy, October 22-24, 2014 Revised Selected Papers
The locus of the best individual is different from the population’s mean. As the evolution advances, the location of both the best individual and of the population’s mean could be closer between themselves. This is used to self-control the population diversity. Figure 1 shows the ﬂow diagrams of a traditional evolutionary algorithm and the MAGO. Following is MAGO’s pseudo code. 1: j = 0, Random initial population generation uniformly distributed over the search space. 2: Repeat. 3: Evaluate each individual with the objective function.
The size of the populations remained unchanged for all the experiments conducted and was set to 200 chromosomes. Every experiment was executed 3 times, so the results presented here are the average of these runs. We should highlight here that, in every experiment, only one clustering criterion c is being optimized. The values of the rest of the criteria are also calculated during every experiment only for evaluation reasons. In other words, the values of the rest of the criteria are not their best Table 1 Datasets used Dataset Duration Movie 1 Movie 2 Folk dances 02 ∶ 06 ∶ 21 01 ∶ 44 ∶ 31 – Classes Size of dataset # of features 21 41 5 1, 222 1, 435 1012 152 × 152 150 × 150 1000 Evolution of Graphs for Unsupervised Learning 29 Fig.
J. Syst. Sci. 43(10), 1785–1804 (2012) 14. : An overview of evolutionary algorithms: practical issues and common pitfalls. Inform. Softw. Tech. 43(14) (2001) 15. , Bengoetxea, E. ): Towards a new evolutionary computation: advances on estimation of distribution algorithms. In: Studies in Fuzziness and Soft Computing, vol. 192. Springer, Heidelberg (2006) 16. : A multi dynamics algorithm for global optimization. Math. Comput. Model. 52(7), 1271–1278 (2010) 17. : Sensitivity analysis of an autonomous evolutionary algorithm.