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 complaints of the twelfth Mexican overseas convention on man made Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the whole of eighty five papers provided in those lawsuits have been rigorously reviewed and chosen from 284 submissions. the 1st quantity offers with advances in synthetic intelligence and its purposes and is dependent within the following 5 sections: common sense and reasoning; knowledge-based platforms and multi-agent structures; common language processing; desktop translation and bioinformatics and scientific functions. the second one quantity bargains 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 platforms; computing device studying and trend reputation; information mining; machine 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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9926. 195221110 . From this expression we solve for M, to get M = {b − ln[ln(a / p )]}/ c . 28 by chance alone is less than five in one hundred. Therefore, it is enough to obtain 85 or more calculate μ and σ with 95% reliability. xi ’s to Fig. 2. Probability of χ 2 and Oi>5 as a function of the number of problems solved In what follows we describe the algorithm which results from all the foregoing considerations. Algorithm for the Determination of the Distribution’s Parameters Select an optimization algorithm A.

Therefore, it is enough to obtain 85 or more calculate μ and σ with 95% reliability. xi ’s to Fig. 2. Probability of χ 2 and Oi>5 as a function of the number of problems solved In what follows we describe the algorithm which results from all the foregoing considerations. Algorithm for the Determination of the Distribution’s Parameters Select an optimization algorithm A. 1. Make G ← number of generations. 2. Generate a random binary string. This is one possible f i ( x) . 3. Minimize f i ( x) iterating A, G times.

In [1] we reported the superior behavior, out of 4 evolutionary algorithms and a hill climber, of a particular breed: the so-called Eclectic Genetic Algorithm (EGA). EGA was tested vs. a set (TS) consisting of large number of selected problems most of which have been used in previous works as an experimental testbed. However, the conclusions of the said benchmark are restricted to the functions in TS. In this work we extend the previous results to a much 31 larger set (U) consisting of ξ ≈  (2 i =1 50 64 i ) ≈ 11 × 10 unconstrained func- tions.

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