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

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

This publication constitutes the refereed complaints of 5 application-oriented workshops held simultaneously as EvoWorkshops 2001 in Como, Italy in April 2001.
The fifty two revised complete papers offered have been conscientiously reviewed and chosen out of seventy five submissions. The papers are geared up in topical sections on graph difficulties, Knapsack difficulties, ant algorithms, task difficulties, evolutionary algorithms research, permutative difficulties, aeronautics, snapshot 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

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Procedure getPath(i, j, pred) pathi ← {i}, pathj ← {j} (ui , vi ) ← (i, pred[i]) (uj , vj ) ← (j, pred[j]) Direct Spanning Tree Representation in GAs 17 path j path j meeting node k subPath(pathj , k,j) = {} pathi = subPath(pathi , i,k) subPath(pathj , k,j) meeting node j(k) path i = subPath(pathi , i,k) j i i Fig. 4. 6 Example of Path Crossover Based on This Implementation We demonstrate the example of path crossover of Figure 1 based this implementation in Figure 5. 4 Application to the OCCST Problem We re-implement the GA presented in [8] for the OCCST problem using the implementation algorithms above.

Late-Breaking-Papers Proc. of the 2000 Genetic and Evolutionary Computation Conference, Las Vegas, NV, pp. 309-316, July 2000. , Bouchebaba Y. A new genetic algorithm for the optical communication spanning tree problem. Proc. of Artifical Evolution 99, LNCS 1829, pp. 162-173, Dunkerque, France, 1999. 8. Gibbons A. Algorithmic graph theory. Cambridge University Press, New York. 9. Michalewicz Z. Genetic Algorithms + Data Structures = Evolutionnary Programs. Springer-Verlag, 3rd edition, 1996. An Evolutionary Algorithm with Stochastic Hill-Climbing for the Edge-Biconnectivity Augmentation Problem Ivana Ljubi´c and G¨ unther R.

The EA’s stochastic hill-climbing. An edge e ∈ S is then redundant and can be removed if: ∀ e0 ∈ Path(e) : ncov (e0 ) ≥ 2. After each edge elimination from S, ncov (e0 ) is updated accordingly. The pseudo-code presented in Figure 2 shows the algorithm in more detail. During hill-climbing, each set Path(e), ∀e ∈ AUG, can be determined in O(|path(e)|) time, if a depth-first search started from an arbitrarily chosen root is performed and depth and parent informations are stored for each node [12].

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