TY - GEN
T1 - An investigation of selection hyper-heuristics in dynamic environments
AU - Kiraz, Berna
AU - Uyar, A. Şima
AU - Özcan, Ender
PY - 2011
Y1 - 2011
N2 - Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.
AB - Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.
UR - https://www.scopus.com/pages/publications/79955842043
U2 - 10.1007/978-3-642-20525-5_32
DO - 10.1007/978-3-642-20525-5_32
M3 - Conference contribution
AN - SCOPUS:79955842043
SN - 9783642205248
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 314
EP - 323
BT - Applications of Evolutionary Computation - EvoApplications 2011
T2 - EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, EvoApplications 2011
Y2 - 27 April 2011 through 29 April 2011
ER -