Thomas Gaube, Franz Rothlauf (auth.), Egbert J. W. Boers's Applications of Evolutionary Computing: EvoWorkshops 2001: PDF

By Thomas Gaube, Franz Rothlauf (auth.), Egbert J. W. Boers (eds.)

ISBN-10: 3540419209

ISBN-13: 9783540419204

This publication constitutes the refereed lawsuits of 5 application-oriented workshops held at the same time as EvoWorkshops 2001 in Como, Italy in April 2001.
The fifty two revised complete papers provided have been rigorously reviewed and chosen out of seventy five submissions. The papers are prepared in topical sections on graph difficulties, Knapsack difficulties, ant algorithms, task difficulties, evolutionary algorithms research, permutative difficulties, aeronautics, photo research and sign processing, evolutionary studying, and evolutionary scheduling and timetabling.

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Extra resources for Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings

Example text

7 times slower than a Dec 500/400). “total” gives the Application of GRASP to the Multiconstraint Knapsack Problem 39 average total execution time of the algorithm whereas “best” gives the average CPU time to find the best solution returned by the algorithm. For data sets with (m, n) = (5, 100) and (m, n) = (10, 100) all but one of the solutions found by CBGA were proven to be optimal using an integer programming solver. For both versions of our GRASP, the average gap for each of the n = 100 data sets is a fraction of 1%.

The latter point caused us to choose the RGGA because a great number of numerical tests for PT/ELS was to be carried out.

In our experiments, the maximum total number of iterations is set to 200 for all of the n = 100 and the n = 250 problem instances, and to 50 for all of the n = 500 problem instances; the number of iterations in the warming period is set to 70, when n = 100 or n = 250, and to 20 when n = 500; the algorithms 38 P. P. A. Maki Table 1. 75 Percentage Relative Error, E GRASP 0 GRASP X ave. min max ave. 39 CPU GRASP 0 GRASP X GA CHU best total best total best total 57 139 26 138 10 346 54 178 62 199 24 347 24 128 29 143 27 362 1349 2458 1152 2640 51 682 1576 3263 1162 2978 277 709 750 2237 816 2190 196 763 34 44 44 64 265 1272 37 65 42 96 391 1346 44 82 98 151 386 1413 103 214 41 166 98 384 114 295 122 287 97 419 86 243 62 206 17 463 1583 3287 1303 3710 359 871 2510 4809 1749 5085 342 932 1749 1708 1027 3433 128 1011 36 57 41 94 703 1505 40 86 75 167 562 1729 33 97 139 231 938 1932 258 606 168 573 177 605 326 948 380 927 118 782 184 698 346 850 90 904 1710 2733 1283 2581 583 1500 1531 3782 1912 3472 902 1980 1500 2703 1011 2504 1059 2441 63 97 241 533 1127 2438 104 169 450 877 1122 3199 100 196 823 1192 1903 3888 stop after 100 iterations without improvement of the best solution when n = 100 or n = 250 and after 30 such iterations when n = 500.

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Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings by Thomas Gaube, Franz Rothlauf (auth.), Egbert J. W. Boers (eds.)


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