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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 e-book 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 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, photograph 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

We present the idea in the context of the Construct Solution function used in 36 P. P. A. Maki Gzero ← is Feasible(S, I); if not Gzero then { S ← N − S; b ← Re − b; } for each i ∈ M do bi ← bi − j∈S rij ; (CS , FS ) ← Update CF(Gzero, M, N − S, R, b ); if Gzero and CS = ∅ then return S ∪ FS ; while FS = ∅ do { l ← α |FS | ; determine the elements θk ∈ FS , k = 1, . . , l, giving rise to the l best benefits with respect to gS ; pick j ∈ {θ1 , θ2 , . . , θl } at random; S ← S ∪ {j}; for each i ∈ CS do bi ← bi − rij ; (CS , FS ) ← Update CF(Gzero, CS , FS − {j}, R, b ); if CS = ∅ then { if Gzero then S ← S ∪ FS ; FS ← ∅; } } if Gzero then return S else return N − S; Fig.

Naval Research Logistics 34 (1987) 161–172 Path Tracing in Genetic Algorithms Applied to the Multiconstrained Knapsack Problem Jens Levenhagen1 , Andreas Bortfeldt2 , and Hermann Gehring2 2 1 Astrium GmbH, 88039 Friedrichshafen, Germany Dept. of Business Informatics, University of Hagen, 58084 Hagen, Germany Abstract. This contribution investigates the usefulness of F. Glover’s path tracing concept within a Genetic Algorithm context for the solution of the multiconstrained knapsack problem (MKP).

GRASP was pioneered by Feo and Resende (see [1]), and is an iterative metaheuristic search process for solving optimization problems. Each iteration in a GRASP consists of a construction phase followed by a local search phase. The best solution generated during the iterative process is kept as the overall result (see figure 1). In a GRASP, we represent a solution as a subset of a given set of components, N , where N is specified as part of a problem instance, I. The idea is then to construct a solution component-by-component.

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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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