TY - GEN
T1 - Increasing the serial and the parallel performance of the CMA-evolution strategy with large populations
AU - Müller, Sibylle D.
AU - Hansen, Nikolaus
AU - Koumoutsakos, Petros
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2002.
PY - 2002/1/1
Y1 - 2002/1/1
N2 - The derandomized evolution strategy (ES) with covariance matrix adaptation (CMA), is modified with the goal to speed up the algorithm in terms of needed number of generations. The idea of the modification of the algorithm is to adapt the covariance matrix in a faster way than in the original version by using a larger amount of the information contained in large populations. The original version of the CMA was designed to reliably adapt the covariance matrix in small populations and turned out to be highly efficient in this case. The modification scales up the efficiency to population sizes of up to 10n, where n ist the problem dimension. If enough processors are available, the use of large populations and thus of evaluating a large number of search points per generation is not a problem since the algorithm can be easily parallelized.
AB - The derandomized evolution strategy (ES) with covariance matrix adaptation (CMA), is modified with the goal to speed up the algorithm in terms of needed number of generations. The idea of the modification of the algorithm is to adapt the covariance matrix in a faster way than in the original version by using a larger amount of the information contained in large populations. The original version of the CMA was designed to reliably adapt the covariance matrix in small populations and turned out to be highly efficient in this case. The modification scales up the efficiency to population sizes of up to 10n, where n ist the problem dimension. If enough processors are available, the use of large populations and thus of evaluating a large number of search points per generation is not a problem since the algorithm can be easily parallelized.
U2 - 10.1007/3-540-45712-7_41
DO - 10.1007/3-540-45712-7_41
M3 - Conference contribution
AN - SCOPUS:84944312119
SN - 3540441395
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 422
EP - 431
BT - Parallel Problem Solving from Nature - PPSN 2002 - 7th International Conference, Proceedings
A2 - Guervos, Juan Julian Merelo
A2 - Adamidis, Panagiotis
A2 - Beyer, Hans-Georg
A2 - Schwefel, Hans-Paul
A2 - Fernandez-Villacanas, Jose-Luis
PB - Springer Verlag
T2 - 7th International Conference on Parallel Problem Solving from Nature, PPSN 2002
Y2 - 7 September 2002 through 11 September 2002
ER -