By Thomas Gaube, Franz Rothlauf (auth.), Egbert J. W. Boers (eds.)
This publication constitutes the refereed court cases 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 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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Additional info for Applications of Evolutionary Computing: EvoWorkshops 2001: EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM Como, Italy, April 18–20, 2001 Proceedings
A new evolutionary algorithm is proposed that works directly on the set of additional links of a candidate solution. Problem-specific initialization, recombination, and mutation operators use a stochastic hill-climbing procedure. With low computational effort, only locally optimal, feasible candidate solutions are produced. Experimental results are significantly better than those of a previous genetic algorithm concerning final solutions’ qualities and especially execution times. 1 Introduction All communication networks are designed for certain demands and requirements.
From the edges from AUG that connect the same components, only the cheapest ones are included and all others are discarded. e. augmenting edges connecting nodes of the same component, are always discarded. For each edge in the shrinked graph a reference back to the corresponding edge of the original problem is stored. After shrinking, the graph has always a tree structure, where all edges represent bridges of the initial configuration. Note that now, GS = (V, E ∪ S) and GA = (V, E ∪ AUG) can become multigraphs, since AUG and E are not necessarily disjoint anymore (see Fig.
12. R. E. Tarjan. Depth first search and linear graph algorithms. SIAM Journal of Computing, 1:146–160, 1972. Application of GRASP to the Multiconstraint Knapsack Problem Pierre Chardaire, Geoff P. McKeown, and Jameel A. uk Abstract. A number of approaches based on GRASP are presented for the Multiconstraint Knapsack Problem. GRASP combines greedy construction of feasible solutions with local search. Results from applying our algorithms to standard test problems are presented and compared with results obtained by Chu and Beasley.
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.)