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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 synthetic Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the entire of eighty five papers awarded in those complaints have been rigorously reviewed and chosen from 284 submissions. the 1st quantity bargains with advances in synthetic intelligence and its functions and is dependent within the following 5 sections: good judgment and reasoning; knowledge-based structures and multi-agent platforms; average language processing; computer translation and bioinformatics and scientific functions. the second one quantity offers with advances in delicate computing and its purposes and is dependent within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever platforms; fuzzy platforms; laptop studying and development attractiveness; info mining; machine imaginative and prescient and photograph 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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It is easy to see that 31 64 i 50 ξ ≈  (2 ) ≈ 11 × 10 . The method outlined provides us with an unlimited i =1 reservoir of functions in ℜ × ℜ . Equally importantly, the random selection of a number τ and the further random selection of τ different indices and τ different ω j ’s yields a uniquely identifiable function from such reservoir. The pool of Walsh functions was randomly generated at the beginning; the f (x ) ’s which the algorithms were required to minimize were all gotten from the same pool, thus allowing us to test the algorithms in a homogeneous functional environment.

If there is always a non-zero probability of exiting a given state of the MC). Holland’s original GA [6] did not include elitism. But most practical implementations do. In fact, any elitist algorithm, even if it is not evolutionary, satisfying the condition of exhaustive visits to all possible states, will, by the same token, reach a global optimum. A case in point is the so-called Random Mutation Hill Climber or RMH (for which see [1, 7, 8]). If we keep track of the best value, however, the behavior of the algorithm may be illustrated as in Figure 1.

That is, for every genome the terms corresponding to the 1’s are calculated. These take the place of the X in (5). Then the AA is applied to get the corresponding coefficients. To each combination of β 1’s there corresponds a set of β coefficients minimizing ε MAX = max(| f i − y i |) ∀i . For this set of coefficients ε RMS is calculated. This is the fitness function for the GA. In the end, we retain the coefficients which best minimize ε RMS (from the GA) out of those which best minimize ε MAX (from the AA).

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