By Abhaya Chandra Kammara, Andreas König (auth.), Václav Snášel, Pavel Krömer, Mario Köppen, Gerald Schaefer (eds.)
This quantity of Advances in clever structures and Computing includes authorised papers awarded at WSC17, the seventeenth on-line global convention on tender Computing in commercial purposes, held from December 2012 to January 2013 on the web. WSC17 keeps a profitable sequence of clinical occasions began over a decade in the past by means of the realm Federation of soppy Computing. It introduced jointly researchers from over the realm drawn to the ever advancing cutting-edge within the box. non-stop technological advancements make this on-line discussion board a doable collecting structure for a global type convention. the purpose of WSC17 was once to disseminate very good study effects and give a contribution to development an international community of scientists drawn to either theoretical foundations and sensible purposes of sentimental computing.
The 2012 version of the web international convention on smooth Computing in commercial purposes consisted of basic music and certain consultation on non-stop good points Discretization for Anomaly Intrusion Detectors new release and particular consultation on rising Theories and purposes in Transportation technology. a complete of 33 prime quality learn papers have been authorised after a rigorous evaluate procedure and are supplied during this book.
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Additional resources for Soft Computing in Industrial Applications: Proceedings of the 17th Online World Conference on Soft Computing in Industrial Applications
The terminal set consists of random numbers, and of the function variables. Standard GP parameters are used, as shown in Table 2. The fitness function is the Mean Squared Error (MSE). 2 Accuracy Evaluation In order to compare the accuracy of standard GP and SGP, we run GP and SGP for 200 generations using a population of 100 individuals on the above described benchmarks. Figure 2a shows the result of the comparison. According to the figure, SGP performs better than the standard GP in terms of accuracy.
This is probably due, in accordance with the results found in the chapter of Burke, to the presence of many local optima. In the case of genotypic diversity (see Fig. 4b) in SGP, in very early generations the correlation between the fitness and genotypic entropy is positive. After these early generations, the correlation becomes lower and close to zero and afterward becomes negative. , a similar behavior have been evidenced. 5 −1 0 50 100 150 200 Generation (b) Fig. 4 a Correlation between fitness and phenotypic diversity, b Correlation between fitness and genotypic diversity Statistical Genetic Programming: The Role of Diversity 47 Our investigation results are similar to those of .
In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001) 19. Michal Wozniak. Experiments with trained and untrained fusers. In Emilio Corchado, Juan Corchado, and Ajith Abraham, editors, Innovations in Hybrid Intelligent Systems, volume 44 of Advances in Soft Computing, pages 144–150. Springer Berlin / Heidelberg, 2007. 20. Wozniak, Michal, Zmyslony, Marcin: Combining classifiers using trained fuser—analytical and experimental results. Neural Netw.