Intelligent Computing Theories and Application: 12th by De-Shuang Huang, Kang-Hyun Jo

By De-Shuang Huang, Kang-Hyun Jo

This two-volume set LNCS 9771 and LNCS 9772 constitutes - along with the quantity LNAI 9773 - the refereed complaints of the twelfth foreign convention on clever Computing, ICIC 2016, held in Lanzhou, China, in August 2016.

The 221 complete papers and 15 brief papers of the 3 court cases volumes have been rigorously reviewed and chosen from 639 submissions. The papers are equipped in topical sections similar to sign processing and snapshot processing; info safeguard, wisdom discovery, and information mining; structures biology and clever computing in computational biology; clever computing in scheduling; details safeguard; advances in swarm intelligence: algorithms and purposes; computer studying and information research for clinical and engineering functions; evolutionary computation and studying; autonomous part research; compressed sensing, sparse coding; social computing; neural networks; nature encouraged computing and optimization; genetic algorithms; sign processing; development attractiveness; biometrics attractiveness; picture processing; info protection; digital truth and human-computer interplay; healthcare informatics conception and strategies; man made bee colony algorithms; differential evolution; memetic algorithms; swarm intelligence and optimization; smooth computing; protein constitution and serve as prediction; advances in swarm intelligence: algorithms and purposes; optimization, neural community, and sign processing; biomedical informatics and snapshot processing; desktop studying; wisdom discovery and usual language processing; nature encouraged computing and optimization; clever regulate and automation; clever information research and prediction; laptop imaginative and prescient; wisdom illustration and specialist approach; bioinformatics.

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Example text

4. Conclusion are shown in last section. 2 MEP Algorithm The standard MEP algorithm adopts a steady state as its underlying mechanism. The MEP algorithm starts by creating a random population of individuals. The following steps are repeated until a stop condition is satisfied. Firstly, two parents are selected using a tournament method selection procedure. Then the parents are recombined by crossover operation to generate two offspring. The offspring are considered for mutation. Finally, the best offspring replaces the worst individual in the current population if the offspring performs better than the worst individual.

Part E: Logist. Transp. Rev. 50, 53–67 (2013) 23. : Application specific instance generator and a memetic algorithm for capacitated arc routing problems. Transp. Res. Part C: Emerg. Technol. 43, 249–266 (2014) 24. : Rank-based memetic algorithm for capacitated arc routing problems. Appl. Soft Comput. 37, 572–584 (2015) 25. : A guided local search heuristic for the capacitated arc routing problem. Eur. J. Oper. Res. 147, 629–643 (2003) A Hybrid Scatter Search Algorithm 15 26. : Heuristics for integer programming using surrogate constraints.

Fig. 2. MREP chromosome MREP genes can be classified into two types: function gene (an element in the function set F) and terminal gene (an element in the terminal set T). The first column of the 2-D chromosome must contain terminals which are randomly selected from the set of terminals T. All other genes are determined by randomly selecting a terminal from the set of functions. A gene that encodes a function contains three parts: one is the operators selected from the function sets, other two parts are two pointers toward the other genes which located in current gene’s previous arbitrary positions in a random layer.

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