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

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 court cases of the twelfth Mexican overseas convention on synthetic Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the full of eighty five papers awarded in those complaints have been conscientiously reviewed and chosen from 284 submissions. the 1st quantity bargains with advances in synthetic intelligence and its functions and is established within the following 5 sections: common sense and reasoning; knowledge-based platforms and multi-agent platforms; average language processing; laptop translation and bioinformatics and scientific purposes. the second one quantity bargains with advances in delicate computing and its functions and is established within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever structures; fuzzy structures; laptop studying and development attractiveness; facts 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 info 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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University of Michigan Press, Ann Arbor (1975) 7. : An Introduction to Genetic Algorithms. MIT Press (1996) 8. : When Will a Genetic Algorithm Outperform Hill Climbing? In: Advances of Neural Information Processing Systems, vol. 6, pp. 51–58. Morgan Kaufmann (1994) 9. : 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) 10. : A Statistical-Genetic Algorithm to Select the Most Significant Features in Mammograms.

Iterate] for i = 1 to G [Evaluate the individual] f(i) ← fitness (xi) if fitness(i)

51–58. Morgan Kaufmann (1994) 9. : 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) 10. : A Statistical-Genetic Algorithm to Select the Most Significant Features in Mammograms. , Hanbury, A. ) CAIP 2007. LNCS, vol. 4673, pp. 189–196. Springer, Heidelberg (2007) 11. : A statistical genetic algorithm. In: Proc. , México, pp. 215–228 (1999) 12. : A universal eclectic genetic algorithm for constrained optimization.

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