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 foreign convention on man made Intelligence, MICAI 2013, held in Mexico urban, Mexico, in November 2013. the full of eighty five papers provided in those court cases have been conscientiously reviewed and chosen from 284 submissions. the 1st quantity offers with advances in man made intelligence and its purposes and is based within the following 5 sections: good judgment and reasoning; knowledge-based platforms and multi-agent platforms; common language processing; laptop translation and bioinformatics and scientific functions. the second one quantity bargains with advances in delicate computing and its purposes and is established within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever platforms; fuzzy structures; computer studying and trend acceptance; 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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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
In Section 3 we describe how the functions in U may be generated and evaluated in ℜ × ℜ , ℜ × ℜ 2and ℜ × ℜ 3. In Section 4 we present our conclusions. 2 Statistical Determination of the Best Algorithm in U A thorough experimental test of a given set of algorithms (A) implies running a large series of minimization trials. The probability that Ai reaches some minimum value (which we denote by κ ) is unknown. These κ will vary for every problem and will distribute with mean μ and standard deviation σ which are also unknown.
Endfor Make best ← I(0) BestFit ← ∞ 2. [Iterate] for i = 1 to G [Evaluate the individual] f(i) ← fitness (xi) if fitness(i) It is possible to outperform the inheritance GA by choosing a higher value of β, although, the algorithm will work with lower conﬁdence than the inheritance GA. Is it possible for the conﬁdence GA to outperform inheritance GA while working with lower conﬁdence level? This will be clariﬁed in the experiments section. 8 beta Fig. 2. 5 and pc = 1 1 36 R. Aguilar-Rivera, M. Valenzuela-Rend´ on, and J. 8 1 beta Fig. 3. 7 and pc = 1 5 Experiments To make a fair comparison, the following experiment is suggested: After a common test problem is selected, the algorithms to compare should be selected.
It is possible to outperform the inheritance GA by choosing a higher value of β, although, the algorithm will work with lower conﬁdence than the inheritance GA. Is it possible for the conﬁdence GA to outperform inheritance GA while working with lower conﬁdence level? This will be clariﬁed in the experiments section. 8 beta Fig. 2. 5 and pc = 1 1 36 R. Aguilar-Rivera, M. Valenzuela-Rend´ on, and J. 8 1 beta Fig. 3. 7 and pc = 1 5 Experiments To make a fair comparison, the following experiment is suggested: After a common test problem is selected, the algorithms to compare should be selected.