By Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, Günther R. Raidl (eds.)
This e-book constitutes the refereed lawsuits of 3 workshops at the program of evolutionary programming and algorithms in quite a few domain names; those workshops have been held along with the fifth eu convention on Genetic Programming, EuroGP 2002, in Kinsale, eire, in April 2002.
The 33 revised complete papers offered have been rigorously reviewed and chosen by means of the respective software committees. according to the 3 workshops EvoCOP, EvoIASP, and EvoSTIM/EvoPLAN, the papers are equipped in topical sections on combinatorial optimization difficulties; snapshot research and sign processing; and scheduling, timetabling, and AI planning.
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Extra info for Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN Kinsale, Ireland, April 3–4, 2002 Proceedings
15. As in the N, K experiments, the bootstrap used B = 1000 resamples. It can be seen that the non-parametric estimators again stay quite close to the true value up to N = 15, but the ML approach underestimates ν throughout. Greater computational resources allowed further experiments with enumeration for N ≤ 24; only estimates could be made for N > 24 (again, using r = 1000 restarts). Figure 4 presents the results of these experiments. Extrapolating from the behaviour for known values of ν, it seems likely that these estimates will be negatively biased.
MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. , Dorigo, M. and Glover, F. ): New Ideas in Optimization. McGraw-Hill, London (1999) 10. Gutjahr, W. : A graph-based Ant System and its convergence. Future Generation Computing Systems. 16 (2000) 873–888 11. Clarke, G. and Wright, J. : Scheduling of vehicles from a central depot to a number of delivery points. Operations Research 12 (1964) 568–581 12. Croes, G. : A method for solving Traveling Salesman Problems.
Jr }. 3. Calculate the bias estimate bias = νˆM L (k, r) − B b=1 νˆb B 4. Output νˆBOOT = νˆM L (k, r) + bias. Fig. 1. Algorithm for the bootstrap estimator of ν. within each class. Thus we can estimate νˆM L (ki , ri ) for each class independently, where ki = βi and ri = iβi . There is one ﬂaw in this idea: when all the optima found are distinct, it is obvious that νˆ(r, r) = ∞. This makes it impossible to give a proper estimate for the number of optima in C1 if C1 = ∅. In order to approximate this value, we can merge the observations corresponding to these optima with those of the next non-empty class Cd , where d = mini>1 (i : Ci = ∅).
Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN Kinsale, Ireland, April 3–4, 2002 Proceedings by Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, Günther R. Raidl (eds.)