TY - GEN
T1 - On-line parameter optimization strategies for metaheuristics
AU - Affenzeller, Michael
AU - Pöllabauer, Lukas
AU - Kronberger, Gabriel
AU - Pitzer, Erik
AU - Wagner, Stefan
AU - Winkler, Stephan
AU - Beham, Andreas
AU - Kofler, Monika
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - In this paper we describe different aspects of parameter tuning strategies for meta-heuristic algorithms. In contrast to many automated parameter adjustment methods in the field, special attention is given to parameter tuning strategies which are able to be applied during the run of a meta-heuristics. The basic idea we are using for this approach steams from self adaptive evolution strategies and this paper discusses different adaptations to this idea in order to find out, to which extent this concept can be transformed to other meta-heuristics.
AB - In this paper we describe different aspects of parameter tuning strategies for meta-heuristic algorithms. In contrast to many automated parameter adjustment methods in the field, special attention is given to parameter tuning strategies which are able to be applied during the run of a meta-heuristics. The basic idea we are using for this approach steams from self adaptive evolution strategies and this paper discusses different adaptations to this idea in order to find out, to which extent this concept can be transformed to other meta-heuristics.
KW - Evolutionary algorithms
KW - Meta-heuristic optimization
KW - Parameter tuning
UR - http://www.scopus.com/inward/record.url?scp=84871302956&partnerID=8YFLogxK
M3 - Conference contribution
SN - 2952474788
SN - 9782952474788
T3 - 22th European Modeling and Simulation Symposium, EMSS 2010
SP - 31
EP - 36
BT - 22th European Modeling and Simulation Symposium, EMSS 2010
T2 - 22th European Modeling and Simulation Symposium, EMSS 2010
Y2 - 13 October 2010 through 15 October 2010
ER -