By Leticia Cervantes, Oscar Castillo, Patricia Melin (auth.), Ildar Batyrshin, Grigori Sidorov (eds.)
The two-volume set LNAI 7094 and 7095 constitutes the refereed lawsuits 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 conscientiously chosen from XXX submissions. the second one quantity includes forty six papers targeting gentle computing. The papers are geared up within the following topical sections: fuzzy good judgment, uncertainty and probabilistic reasoning; evolutionary algorithms and different naturally-inspired algorithms; info mining; neural networks and hybrid clever structures; and machine imaginative and prescient and photograph processing.
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Extra info for Advances in Soft Computing: 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, November 26 - December 4, 2011, Proceedings, Part II
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”. The method is a general one, capable of being applied to an extremely wide range of problems. The algorithms are simple to understand and the required computer code easy to write. GAs were in essence proposed by John Holland in the 1960's.
Xn ) may be written in a language for which an automatic diﬀerentiation tool is not available, or a program is only available as an executable ﬁle, with no source code at hand. , to apply it 18 M. Ceberio and V. Kreinovich to diﬀerent inputs and use the results of this application to compute the desired value Δ. Such black-box methods are based on the fact that, by deﬁnition, the derivative is a limit: ∂f ci = = ∂xi f (x1 , . . , xi−1 , xi + h, xi+1 , . . , xn ) − f (x1 , . . , xi−1 , xi , xi+1 , .
Interval versions of statistical techniques, with applications to environmental analysis, bioinformatics, and privacy in statistical databases. Journal of Computational and Applied Mathematics 199(2), 418–423 (2007) 11. : Towards combining probabilistic and interval uncertainty in engineering calculations: algorithms for computing statistics under interval uncertainty, and their computational complexity. Reliable Computing 12(6), 471–501 (2006) 12. : Fast algorithms for computing statistics under interval uncertainty: an overview.