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Generic Metaheuristics. Edition No. 1
VDM Publishing House, Sep 2008, Pages: 92
In this book a generic library of efficient
metaheuristics for combinatorial optimization is
presented. In the version at hand classes that
feature local search, simulated annealing, tabu
search, guided local search and greedy randomized
adaptive search procedure were implemented.
Most notably a generic implementation features the
advantage that the problem dependent classes and
methods only need to be realized once without
targeting a specific algorithm because these parts of
the source code are shared among all present
algorithms contained in EAlib.
This main advantage is then exemplary demonstrated
with the quadratic assignment problem. The source
code of the QAP example can also be used as an
commented reference for future problems.
Concluding the experimental results of the individual
metaheuristics reached with the presented
implementation are presented.
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