Download Applications of Evolutionary Computing: EvoWorkshops 2002: by Peter Cowling, Graham Kendall, Eric Soubeiga (auth.), PDF

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 complaints of 3 workshops at the software of evolutionary programming and algorithms in a number of domain names; those workshops have been held together with the fifth eu convention on Genetic Programming, EuroGP 2002, in Kinsale, eire, in April 2002.
The 33 revised complete papers provided have been rigorously reviewed and chosen through 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; picture research and sign processing; and scheduling, timetabling, and AI planning.

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Extra resources for Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN Kinsale, Ireland, April 3–4, 2002 Proceedings

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Various heuristic methods have been proposed, such as mincut-based heuristics and orthogonal recursive bisection. List-based priority schedulers have long been the predominant class of static scheduling algorithms [9]. Here, priorities are assigned to tasks following some rule. Most of them are oriented to critical path, most immediate processor first priority. List scheduling heuristics were originally devised with zero inter-task communication costs, and even under this simplified assumption the problem remains NP-hard.

In Total Jackknife Bootstrap 1200 800 400 0 2 4 6 8 10 12 Fig. 2. N, K landscapes for N = 15: Estimates of ν based on the Schnabel census/maximum likelihood approach contrasted with the non-parametric estimates. Also shown are the total number of optima obtained by enumeration and the observed number (k) in r = 1000 restarts. that the global optimum has a large basin of attraction, and is located ‘near’ to other (local) optima. The class of ‘low autocorrelation binary sequences’ (LABS), however, is notorious for generating problems where the global optima are very hard to find.

We have described two general approaches, the SGA and the CE methods, for updating pheromone values for the ACO metaheuristic. These two methods provide systematic, theoretically founded ways for deriving the update rules. Moreover, we have also shown that in many cases the updates used by the two methods are quite similar (or even identical in some cases), and sometimes they coincide with existing ACO updates. While the newly derived pheromone updates do have a solid theoretical motivation, little can be said a priori about their performance as compared to the existing update rules.

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