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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