Get Advances in Soft Computing: 10th Mexican International PDF

By Leticia Cervantes, Oscar Castillo, Patricia Melin (auth.), Ildar Batyrshin, Grigori Sidorov (eds.)

ISBN-10: 3642253296

ISBN-13: 9783642253294

The two-volume set LNAI 7094 and 7095 constitutes the refereed court cases of the tenth Mexican overseas convention on synthetic Intelligence, MICAI 2011, held in Puebla, Mexico, in November/December 2011. The ninety six revised papers offered have been rigorously chosen from XXX submissions. the second one quantity includes forty six papers targeting gentle computing. The papers are equipped within the following topical sections: fuzzy common sense, uncertainty and probabilistic reasoning; evolutionary algorithms and different naturally-inspired algorithms; information mining; neural networks and hybrid clever platforms; and desktop imaginative and prescient and snapshot processing.

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Extra resources for Advances in Soft Computing: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4, 2011, Proceedings, Part II

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Thus, the quality of a compression can be described as the length of the shortest program p that generates x. , [14]: def K(x) = min{len(p) : p generates x}. In unusually good situations, computations are unusually complex. The smaller the Kolmogorov complexity K(x), the more we can compress the original sequence x. , [14]) that, for most strings, the Kolmogorov complexity K(x) is approximately equal to their length – and can, thus, be efficiently computed (as long as we are interested in the approximate value of K(x), of course).

3 Third Case Study: When Computing Variance under Interval Uncertainty Is NP-Hard Computing the range of variance under interval uncertainty is NP-hard: reminder. The above two examples are based on the result that computing the range of a quadratic function under interval uncertainty is NP-hard. Actually, as we have mentioned, even computing the range [V , V ] of the variance V (x1 , . . , xn ) on given intervals x1 , . . , xn is NP-hard [2,3]. Specifically, it turns out that while the lower endpoint V can be computed in polynomial time, computing the upper endpoint V is NP-hard.

A Type-2 Fuzzy System is continuous if and only if both its UMF and its LMF are continuous Type-1 Fuzzy Systems [38]. 3 GAs Genetic algorithms (GAs) are numerical optimization algorithms inspired by both natural selection and genetics. We can also say that the genetic algorithm is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness”[15].

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Advances in Soft Computing: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4, 2011, Proceedings, Part II by Leticia Cervantes, Oscar Castillo, Patricia Melin (auth.), Ildar Batyrshin, Grigori Sidorov (eds.)


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