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Generic Metaheuristics
A Presentation of a Generic Library for Metaheuristics and its Application to the Quadratic Assignment Problem
Engleski
125 b
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In this book a generic library of efficientmetaheuristics for combinatorial optimization ispresented. In the version at hand classes thatfeature local search, simulated annealing, tabusearch, guided local search and greedy randomizedadaptive search procedure were implemented.Most notably a generic implementation features theadvantage that the problem dependent classes andmethods only need to be realized once withouttargeting a specific algorithm because these parts ofthe source code are shared among all presentalgorithms contained in EAlib.This main advantage is then exemplary demonstratedwith the quadratic assignment problem. The sourcecode of the QAP example can also be used as ancommented reference for future problems.Concluding the experimental results of the individualmetaheuristics reached with the presentedimplementation are presented.
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