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 lawsuits of the twelfth Mexican overseas convention on man made Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the entire of eighty five papers awarded in those court cases have been rigorously reviewed and chosen from 284 submissions. the 1st quantity offers with advances in synthetic intelligence and its purposes and is established within the following 5 sections: good judgment and reasoning; knowledge-based structures and multi-agent platforms; common language processing; computer translation and bioinformatics and clinical functions. the second one quantity offers with advances in delicate computing and its functions and is dependent within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever structures; fuzzy structures; computing device studying and trend acceptance; information mining; machine imaginative and prescient and picture processing; robotics, making plans and scheduling and emotion detection, sentiment research and opinion mining.

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Additional 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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Duplicate Population] for j = 1 to n I(n+j) ← I(j) fitness(n+j) ← fitness(j) endfor The Best Genetic Algorithm II 19 5. [ Deterministic Selection Annular Crossover] for j=1 to n/2 Generate a uniform random number 0 ≤ ρ < 1 If ρ ≤ Pc Generate a random number 1 ≤ ρ < L / 2 Interchange the semi-ring starting at locus and I(n-j-1) endif endfor 5. [Mutation] for j=1 to B2M Generate uniform random numbers 0 ≤ ρ1 , ρ 2 < 1 ρ between I(j) Mutate Bit ρ 2 L  of I( ρ1 n ) endFor 6. [Evaluate the New Individuals] Calculate fitness(xi) for i=1,…,n 7.

1 (1998) The Best Genetic Algorithm I 15 27. : A methodology for the statistical characterization of genetic algorithms. C. ) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 79–88. Springer, Heidelberg (2002) 28. : Evolutionary algorithms in theory and pactice: evolution strategies, evolutionary programming, genetic algorithms, ch. 4, pp. 149–159 (1996) 29. : An Introduction to Genetic Algorithms. MIT Press (1996) 30. : The Walsh Transform and the Theory of the Simple Genetic Algorithm. , Wang, P. ) Genetic Algorithms for Pattern Recognition.

In the sphere problem, the trade-off between working with higher α value (faster algorithm) and working with higher confidence difference (better estimation precision) is more critical. Finally, in tables 1 to 3, the p-value of a statistical t-test is presented. The null hypothesis H0 holds the mean value of the best-so-far curve stabilization time (the moment the curve reaches its maximum)of the confidence-controlled algorithm is the same than the other algorithms (not real improvement). H1 holds the other algorithms have higher stabilization time, therefore, confidencecontrolled savings are significant.

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