By Marcin Czajkowski, Marek Kretowski (auth.), Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)
The two-volume set LNAI 7894 and LNCS 7895 constitutes the refereed lawsuits of the twelfth overseas convention on man made Intelligence and gentle Computing, ICAISC 2013, held in Zakopane, Poland in June 2013. The 112 revised complete papers awarded including one invited paper have been rigorously reviewed and chosen from 274 submissions. The fifty six papers incorporated within the moment quantity are equipped within the following topical sections: evolutionary algorithms and their purposes; information mining; bioinformatics and scientific purposes; agent structures, robotics and regulate; man made intelligence in modeling and simulation; and numerous difficulties of man-made intelligence.
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Additional info for Artificial Intelligence and Soft Computing: 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part II
14 M. Gabryel et al. If the system is not empty at the vacation completion epoch, then the service process is being initialized immediately and the new busy period begins. We assume that sequences of successive inter arrival times, service times and the single vacation duration are totally independent random variables. Let us introduce the following notations: • τ1 — the ﬁrst busy period of the system (starting at t = 0); • δ1 — the ﬁrst idle time of the system (consisting of the ﬁrst vacation time v1 and the ﬁrst server standby time q1 ); • h(τ1 ) — the number of packets completely served during τ1 ; • X(t) — the number of packets present in the system at time t.
1 yia xi (λ1 ) ⎡ ... .. ⎤ xj1 (λn ) j x2 (λn ) ⎥ ⎥ ⎥, .. ⎦ . xji (λn ) (7) where xji (λn ) is the i-th sample of matter absorbance in the wavelength λn and yia is the concentration of a in the i-th sample. Where the relation between the absorbance and concentration can be estimate by a coeﬃcient matrix β that multiply X for to obtain Yˆ estimate. The matrix X and Y are divided in Xcal and Ycal for obtain the coeﬃcient matrix β and Xteste and Yteste are used to test the accuracy of prediction model.
These problems represent the class of multi-objective optimization problems. Also, in most of the problems, the objectives are conﬂicting among each other. In thesecases the optimization algorithm should consider each objective as a ﬁtness function. Multi-objective Genetic Algorithm (MOGA) have been applied to the multiobjective problems. VEGA (Vector Evaluated Genetic Algorithm) was the ﬁrst MOGA proposed by Schaﬀer in 1985 . The main diﬀerence between MOGA and standard GAs is the selection operator.
Artificial Intelligence and Soft Computing: 12th International Conference, ICAISC 2013, Zakopane, Poland, June 9-13, 2013, Proceedings, Part II by Marcin Czajkowski, Marek Kretowski (auth.), Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)