TY - GEN
T1 - Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed
AU - Auger, Anne
N1 - Publisher Copyright:
© 2009 ACM.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In this paper, we benchmark an independent-restart (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed. The maximum number of function evaluations used equals 106 times the dimension of the search space. The algorithm could only solve 3 functions with moderate noise in 5-D and 2 functions in 20-D.
AB - The (1+1)-ES with one-fifth success rule is one of the first and simplest stochastic algorithm proposed for optimization on a continuous search space in a black-box scenario. In this paper, we benchmark an independent-restart (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed. The maximum number of function evaluations used equals 106 times the dimension of the search space. The algorithm could only solve 3 functions with moderate noise in 5-D and 2 functions in 20-D.
KW - Benchmarking
KW - Black-box optimization
KW - Evolutionary computation
U2 - 10.1145/1570256.1570343
DO - 10.1145/1570256.1570343
M3 - Conference contribution
AN - SCOPUS:85026384494
SN - 9781605583259
T3 - Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
SP - 2453
EP - 2457
BT - Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
PB - Association for Computing Machinery
T2 - 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Y2 - 8 July 2009 through 12 July 2009
ER -