Andreas Beham, Michael Affenzeller, Stefan Wagner,
"Instance-Based Algorithm Selection on ƒquadratic Assignment Problem Landscapes"
: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, Deutschland, 2017, 2017
Original Titel:
Instance-Based Algorithm Selection on ƒquadratic Assignment Problem Landscapes
Sprache des Titels:
Englisch
Original Buchtitel:
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, Deutschland, 2017
Original Kurzfassung:
Among the many applications of €tness landscape analysis a prominent
example is algorithm selection. Œe no-free-lunch (NFL) theorem
has put a limit on the general applicability of heuristic search
methods. Improved methods can only be found by specialization
to certain problem characteristics which limits their application
to other problems. Œis creates a very interesting and dynamic
€eld for algorithm development. However, this also leads to the
de€nition of a large range of di‚erent algorithms that are hard
to compare exhaustively. An additional challenge is posed by the
fact that algorithms have parameters and thus to each algorithm
there may be a large number of instances. In this work the application
of algorithm selection to problem instances of the quadratic
assignment problem (QAP) is discussed.