TY - GEN
T1 - Fitness landscape based parameter estimation for robust taboo search
AU - Beham, Andreas
AU - Pitzer, Erik
AU - Affenzeller, Michael
N1 - Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Introduction: Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the algorithms' performances with respect to different problem- and problem instance characteristics.
AB - Introduction: Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the algorithms' performances with respect to different problem- and problem instance characteristics.
UR - http://www.scopus.com/inward/record.url?scp=84892612740&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-53856-8_37
DO - 10.1007/978-3-642-53856-8_37
M3 - Conference contribution
SN - 9783642538551
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 292
EP - 299
BT - Computer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
PB - IUCTC Las Palmas de Gran Canaria
T2 - 14th International Conference on Computer Aided Systems Theory, Eurocast 2013
Y2 - 10 February 2013 through 15 February 2013
ER -