Title :
A restart CMA evolution strategy with increasing population size
Author :
Auger, Anne ; Hansen, Nikolaus
Author_Institution :
CoLab Computational Lab., ETH, Zurich, Switzerland
Abstract :
In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.
Keywords :
covariance matrices; evolutionary computation; optimisation; covariance matrix adaptation; increasing population size; local restart strategy; parameter optimization; restart CMA evolution; Convergence; Covariance matrix; Design optimization; Equations; Evolutionary computation; Gaussian distribution; Genetic mutations; Laboratories; Performance evaluation; Testing;
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
DOI :
10.1109/CEC.2005.1554902
