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 offered in those complaints have been rigorously reviewed and chosen from 284 submissions. the 1st quantity bargains with advances in synthetic intelligence and its purposes and is dependent within the following 5 sections: good judgment and reasoning; knowledge-based structures and multi-agent platforms; traditional language processing; computer translation and bioinformatics and clinical purposes. the second one quantity offers with advances in gentle computing and its functions and is established within the following 8 sections: evolutionary and nature-inspired metaheuristic algorithms; neural networks and hybrid clever platforms; fuzzy platforms; laptop studying and trend popularity; info mining; desktop imaginative and prescient and snapshot processing; robotics, making plans and scheduling and emotion detection, sentiment research and opinion mining.
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Additional 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
The crossover component of a GA is actually responsible for the convergence speed of the process. Because of this we want to analyze several possible alternative GAs in trying to determine which is most effective. F. Kuri-Morales, E. Aldana-Bobadilla, and I. López-Peña Fig. 1. Convergence with Elitism a) An elitist canonical GA (in what follows referred to as TGA [eliTist GA]) . b) A Cross generational elitist selection, Heterogeneous recombination, and Cataclysmic mutation algorithm (CHC algorithm) .
We make NS ← 50. We then generate NS instances of N(0,1) and count the number of times the value of the instance is in every decile. We calculate the value of the corresponding χ 2 and store it. We thusly calculate 100,000 combinations of size NS. Out of these combinations we count those for which χ 2 ≤ χ best AND there are at least omin =5 observations per decile. This number divided by 100,000 (which we shall call p) is the experimental probability that, for NS observations, χ 2 “performs” as required.
However, this case is different. We want to ensure the likelihood that xi ’s IS normal. In order to do this we performed the following Monte Carlo experiment. We set a desired probability P that the xi ’s are the observed behavior of the normal. We establish a best desired value of χ 2 which we will call χ best. We make NS ← 50. We then generate NS instances of N(0,1) and count the number of times the value of the instance is in every decile. We calculate the value of the corresponding χ 2 and store it.