By Ying Tan, Yuhui Shi, Carlos A Coello Coello
This e-book and its spouse quantity, LNCS vol. 8794 and 8795 represent the court cases of the fifth foreign convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers provided have been conscientiously reviewed and chosen from 198 submissions. The papers are equipped in 18 cohesive sections, three particular classes and one aggressive consultation masking all significant subject matters of swarm intelligence learn and improvement akin to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; synthetic bee colony algorithms; synthetic immune procedure; evolutionary algorithms; neural networks and fuzzy tools; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; type tools; GPU-based equipment; scheduling and direction making plans; instant sensor networks; energy method optimization; swarm intelligence in picture and video processing; purposes of swarm intelligence to administration difficulties; swarm intelligence for real-world application.
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Additional info for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II
One with the best generalizing capacity will give the best detection efficiency. First we divide the data set into training and testing sets at 70% and 30% by taking 398 vectors as training data set and rest as testing data set. Then data set is used to train and the test the ensemble model. The results are measured against the TP (true positive), TN(true negative), FP(false positive) and FN (false negative). The various performance measures are summarized in the table 1. Evolutionary Ensemble Model for Breast Cancer Classification 13 Table 1.
S´anchez-Crisostomo et al. Table 3. 000000 No. of classes ignored by the classifier 3 2 1 1 1 2 2 3 1 2 2 1 Table 4. 483704 No. of classes ignored by the classifier 0 0 0 1 1 1 2 2 3 0 1 2 2 3 than S11. Similar situations were observed in AU 1P with S8 and S10, M F M with S1 and S2, AU N U with S4 and S8, M AvA with S8 and S11, and AU 1U with S8 and S11 (see Table 3). On Table 4 this behavior was observed in AU N U and AU 1P with B4 and B5. AU N U with A3 and A5. A dramatic situation was noticed in Table 4, we observe that in some datasets the classifier presents better results when does not classify one or more classes that when it classify all classes.
In the swarming behavior, when the AF current state is X i , it will assemble in groups naturally in the moving process. Let X c represent the center n position in its visual scope. If Yi < Yc and f < δ , it denotes the center position has n higher food concentration and is not crowded. It moves a step toward the center position. Otherwise, it performs the preying behavior. The center position X c of m fishes is defined as 1, X c (i ) = 0, m m 2 k =1 m m X k (i ) ≤ 2 k =1 X k (i ) ≥ i = 1, 2,3, , D (9) Preying Behavior.