By Juan Julian Merelo, Agostinho Rosa, José M. Cadenas, António Dourado, Kurosh Madani, Joaquim Filipe
The current e-book features a set of chosen prolonged papers from the 6th overseas Joint convention on Computational Intelligence (IJCCI 2014), held in Rome, Italy, from 22 to 24 October 2014. The convention used to be composed via 3 co-located meetings: The overseas convention on Evolutionary Computation conception and purposes (ECTA), the overseas convention on Fuzzy Computation conception and purposes (FCTA), and the overseas convention on Neural Computation concept and purposes (NCTA). fresh progresses in medical advancements and purposes in those 3 parts are mentioned during this publication. IJCCI bought 210 submissions, from fifty one international locations, in all continents. After a double blind paper assessment played by means of this system Committee, 15% have been accredited as complete papers and therefore chosen for oral presentation. extra papers have been authorized as brief papers and posters. yet another choice was once made after the convention, established additionally at the review of presentation caliber and viewers curiosity, in order that this ebook contains the prolonged and revised types of the superior papers of IJCCI 2014. dedication to top quality criteria is an incredible drawback of IJCCI that might be maintained within the subsequent variants, contemplating not just the stringent paper recognition ratios but additionally the standard of this system committee, keynote lectures, participation point and logistics.
Read or Download Computational Intelligence: International Joint Conference, IJCCI 2014 Rome, Italy, October 22-24, 2014 Revised Selected Papers PDF
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Extra resources for Computational Intelligence: International Joint Conference, IJCCI 2014 Rome, Italy, October 22-24, 2014 Revised Selected Papers
Spectral graph clustering  refers to a class of graph techniques, that rely on eigenanalysis of the Laplacian matrix of a similarity graph, aiming to divide graph nodes in disjoint clusters. In all clustering techniques, and thus also in spectral clustering, nodes that originate from the same cluster should have high similarity values, whereas nodes from diﬀerent clusters should have low similarity values. In  the authors summarize some of the applications of spectral graph clustering. So far, some evolutionary-based approaches to the problem of clustering have been proposed throughout the years.
We append 01 or 011 to the back of c such that size(c) = N . 40 D. Y. 4 Motif-Preserving Crossover Operator If a chromosome has special structural property, we can apply an adapted onepoint crossover operator that preserves this propose. The motif-preserving one-point crossover operator is shown in Algorithm 4. Algorithm 4: Motif-preserving one-point crossover operator. procedure SPXover(c1 , c2 ) c1 ← auxRepre(c1 ) c2 ← auxRepre(c2 ) x Pt ← chooseXPt(c1 , c2 ) xover(c1 , c2 , x Pt) c1 ← fixChromosome(c1 ) c2 ← fixChromosome(c2 ) c1 ← auxToOriginal(c1 ) c2 ← auxToOriginal(c2 ) end procedure Here the function chooseXPt decides the crossover point in the auxiliary representation such that the two children chromosomes in the original representation are either one bit longer or shorter than N.
The probability pi of the ith chromosome to be selected, if fi is its ﬁtness value is deﬁned as: f (6) pi = N i . 7, in order to produce new ones. In the proposed algorithm, a single crossover point is randomly selected for every set of chromosomes and the subsequences that are formed are exchanged respectively. 4, to undergo mutation, that is the random change of some elements of a chromosome. In order to guarantee that the newly produced chromosomes will not have been altered too much we perform mutation by converting 1 % of 0’s to 1’s and vice versa.
Computational Intelligence: International Joint Conference, IJCCI 2014 Rome, Italy, October 22-24, 2014 Revised Selected Papers by Juan Julian Merelo, Agostinho Rosa, José M. Cadenas, António Dourado, Kurosh Madani, Joaquim Filipe