By Angel Kuri-Morales, Edwin Aldana-Bobadilla (auth.), Félix Castro, Alexander Gelbukh, Miguel González (eds.)

The two-volume set LNAI 8265 and LNAI 8266 constitutes the complaints of the twelfth Mexican overseas convention on synthetic Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the complete of eighty five papers awarded in those complaints have been rigorously reviewed and chosen from 284 submissions. the 1st quantity bargains with advances in synthetic intelligence and its functions and is dependent within the following 5 sections: common sense and reasoning; knowledge-based platforms and multi-agent platforms; typical language processing; computing device translation and bioinformatics and scientific functions. the second one quantity offers with advances in tender computing and its purposes and is established within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever platforms; fuzzy platforms; computer studying and trend popularity; information mining; computing device imaginative and prescient and photo processing; robotics, making plans and scheduling and emotion detection, sentiment research and opinion mining.

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Extra resources for Advances in Soft Computing and Its Applications: 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Mexico City, Mexico, November 24-30, 2013, Proceedings, Part II

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80 (released December 2006), http://www. html 18. : An experimental study of Benchmarking functions for genetic algorithms. Intern. J. Computer Math. 79(4), 403–416 (2002) 19. : Generalizing the notion of schema in genetic algorithms. Artificial Intelligence 50(3), 385–396 (1991) 20. : The CHC Adaptive Search Algorithm. How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In: Rawlins, G. ) FOGA-1, pp. 265–283. Morgan Kaufmann (1991) 21. : Convergence Analysis of Canonical Genetic Algorithms.

V n ) y = f ( v) (4) The approximant is defined to have the following form: y = c 1 X 1 + c 2 X 2 + ... + c m X m (5) Xi denotes a combination of the independent variables. That is, Xi = fi(v). According to the way these combinations are defined one may obtain different approximants. Now, from the universal approximation theorem [15], any function of n variables may be approximated with at most   k ( 2 j − 1 + n)!    2i − 1 +   k   j =1 ( 2 j − 1)! n!    j =1 ( 2 j − 1)! n!     (6) terms of degree k.

Due to the conditions of the test, even a small difference is significant. It can be said all these algorithms can be configured to work faster. For example, it is possible to configure the inheritance GA for higher estimation levels, making it able to achieve the promised performance saving of 70% (Smith [6]). Nevertheless, that configuration would make the inheritance GA unable to abide to the conditions of the proposed experiments. The results suggest the use of confidence leads to a more robust algorithm.

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Advances in Soft Computing and Its Applications: 12th by Angel Kuri-Morales, Edwin Aldana-Bobadilla (auth.), Félix
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