Download e-book for kindle: 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.)

ISBN-10: 3540419209

ISBN-13: 9783540419204

This e-book 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 awarded 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, photo research and sign processing, evolutionary studying, and evolutionary scheduling and timetabling.

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Read Online or Download Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings PDF

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

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Such an initial solution could, for example, be selected at random or according to some heuristic. If S0 is feasible, we proceed as in GRASP 0, seeking to construct an improved feasible solution. Otherwise, we proceed as in GRASP 1, seeking to construct a feasible solution starting from the infeasible solution, S0 . A transformed specification for Construct Solution that takes an initial solution, S0 , as a parameter and which covers both the feasible and infeasible cases is given in figure 5. Note that the all-zero GRASP 0 and the all-one GRASP 1 cases are both special cases of this more general algorithm.

5]), using edges from the shrinked set AUG and E. A feasible set of augmenting edges S ⊂ AUG can finally be derived from the set of edges included in this branching. It is proven that this algorithm determinates a solution with costs no greater than two times the costs of the optimal solution. The time complexity of this algorithm has been improved by Khuller and Thurimella [6]. Recently, Zhu et al. [7,8] proposed an iterative scheme based on the branching algorithm. They provide a heuristic formula for measuring how good a certain augmenting edge in a determined outgoing branching can be.

Zhu, S. Khuller, and B. Raghavachari. A uniform framework for approximating weighted connectivity problems. In Proceedings of the 10th ACM-SIAM Symposium on Discrete Algorithms, pages 937–938, 1999. 9. J. Kratica. Improving performances of the genetic algorithm by caching. Computers and Artificial Intelligence, 18(3):271–283, 1999. 10. W. Mader. Minimale n-fach kantenzusammenh¨ angende Graphen. Math. , 191:21–28, 1971. 11. P. Moscato. Memetic algorithms: A short introduction. In D. , editor, New Ideas in Optimization, pages 219–234.

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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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