Advances in Swarm Intelligence: 5th International - download pdf or read online
By Ying Tan, Yuhui Shi, Carlos A Coello Coello
This publication and its spouse quantity, LNCS vol. 8794 and 8795 represent the court cases of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers awarded have been rigorously reviewed and chosen from 198 submissions. The papers are prepared in 18 cohesive sections, three precise periods and one aggressive consultation overlaying all significant subject matters of swarm intelligence examine and improvement similar 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 process; evolutionary algorithms; neural networks and fuzzy tools; hybrid equipment; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; type tools; GPU-based tools; scheduling and direction making plans; instant sensor networks; energy method optimization; swarm intelligence in picture and video processing; functions of swarm intelligence to administration difficulties; swarm intelligence for real-world application.
Read or Download Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II PDF
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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
This is the phenotype problem representation. The population may be represented using any of double vector or a bit string representation. The Genetic Operators include Selection, Crossover, Eliticism, Mutation, etc. The Genetic Operators ensured creation of good individuals from one population to the other. Let us assume that there was a single hidden layer consisting of H neurons. The input and output layers have I an O neurons respectively. In this system, it may easily be seen that there are I x H weights between the input layer and the hidden layer and H x O weights between the hidden layer and the output layer, this makes the total number of weights as W=I x H + H x O.
Section 3 gives the methodology used in tackling the problem. Experimental results are presented in Section 4. Conclusion and future work are given in the last section. 2 Related Work A classification system is one that actually maps input vectors to a specific class. Hence, classification is basically the job of learning the procedure that maps the input data . This has, in turn, has enthused researchers to replicate this success in the field of medical diagnostics. Their efforts have bore significant gains through the application of several standards and techniques of pattern recognition to the said problem .
Neurocomput. 92, 170–182 (2012) 11. : A shapelet transform for time series classification. In: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp. 289–297 (2012) 12. : Facing the reality of data stream classification: coping with scarcity of labeled data. Knowledge and Information Systems 33(1), 213–244 (2012) 13. : Dynamic classifier ensemble using classification confidence. Neurocomput. 99(1), 581–591 (2013) 14. : Semi-Supervised Classification on Evolutionary Data.
Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II by Ying Tan, Yuhui Shi, Carlos A Coello Coello