TY - GEN
T1 - Recombination for learning strategy parameters in the MO-CMA-ES
AU - Voß, Thomas
AU - Hansen, Nikolaus
AU - Igel, Christian
PY - 2010/12/1
Y1 - 2010/12/1
N2 - The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population of candidate solutions, which are varied by additive, zero-mean Gaussian mutations. Each individual learns its own covariance matrix for the mutation distribution considering only its parent and offspring. However, the optimal mutation distribution of individuals that are close in decision space are likely to be similar if we presume some notion of continuity of the optimization problem. Therefore, we propose a lateral (inter-individual) transfer of information in the MO-CMA-ES considering also successful mutations of neighboring individuals for the covariance matrix adaptation. We evaluate this idea on common bi-criteria objective functions. The preliminary results show that the new adaptation rule significantly improves the performance of the MO-CMA-ES.
AB - The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population of candidate solutions, which are varied by additive, zero-mean Gaussian mutations. Each individual learns its own covariance matrix for the mutation distribution considering only its parent and offspring. However, the optimal mutation distribution of individuals that are close in decision space are likely to be similar if we presume some notion of continuity of the optimization problem. Therefore, we propose a lateral (inter-individual) transfer of information in the MO-CMA-ES considering also successful mutations of neighboring individuals for the covariance matrix adaptation. We evaluate this idea on common bi-criteria objective functions. The preliminary results show that the new adaptation rule significantly improves the performance of the MO-CMA-ES.
U2 - 10.1007/978-3-642-01020-0_16
DO - 10.1007/978-3-642-01020-0_16
M3 - Conference contribution
AN - SCOPUS:78650751826
SN - 3642010199
SN - 9783642010194
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 155
EP - 168
BT - Evolutionary Multi-Criterion Optimization - 5th International Conference, EMO 2009, Proceedings
T2 - 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009
Y2 - 7 April 2009 through 10 April 2009
ER -