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

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 man made Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the entire of eighty five papers offered in those complaints have been rigorously reviewed and chosen from 284 submissions. the 1st quantity offers with advances in man made intelligence and its functions and is dependent within the following 5 sections: common sense and reasoning; knowledge-based structures and multi-agent structures; traditional language processing; desktop translation and bioinformatics and clinical purposes. the second one quantity offers with advances in smooth 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; computer studying and development attractiveness; facts mining; laptop imaginative and prescient and photo processing; robotics, making plans and scheduling and emotion detection, sentiment research and opinion mining.

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Read or Download 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 PDF

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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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Under these conditions, the performance is more in accordance with the results reported by Sastry [5], which are more conservative. 38 R. Aguilar-Rivera, M. Valenzuela-Rend´ on, and J. 5 15 0 100 200 300 400 Evaluations 500 600 700 Fig. 4. Best-so-far curves of algorithms for the 20 bits onemax problem Best−so−far curve of Inheritance−based Algorithms 40 38 Fitness 36 34 32 GA Inheritance Confidence−Controlled 30 28 0 500 1000 1500 Evaluations 2000 2500 Fig. 5. Best-so-far curves of algorithms for the 40 bits onemax problem An important fact is that the values of α and β allowed the confidence GA to work with higher confidence than the inheritance GA for every test presented.

Kuri-Morales, E. Aldana-Bobadilla, and I. López-Peña even for modest values of p and β an exhaustive search is out of the question. This optimization problem may be tackled using a genetic algorithm (GA), as follows. The genome is a binary string of size p. Every bit in it represents a monomial. These monomials are ordered as per the sequence of the consecutive powers of the variables. If the bit is ‘1’ it means that the corresponding monomial remains while if it is a ‘0’ it means that such monomial is not to be considered.

To achieve higher evaluation savings, a higher α value is needed, and this will cause higher uncertainty in the evaluations which could be harmful if not handled properly. There could be several ways to compute confidence. Basically, confidence should be a quantity which decreases when inheritance takes places and should be a function of parents confidence. Following the same ideas of inheritance an analogous expression for confidence can be proposed cc = β cp1 + cp2 2 . (4) In equation 4, the parameter β ∈ (0, 1) and can be understood as a decay factor.

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