Model complex control CMA-ES

Tong，Xin1; Yuan，Bo2; Li，Bin1

2019-11-01

10.1016/j.swevo.2019.100558

Swarm and Evolutionary Computation   影响因子和分区

2210-6502

2210-6510

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown great performance on nonseparable optimization problems largely due to its rotation-invariant feature. However, as the computational cost of the self-adaption operation is sensitive to the scale of problems, the performance of CMA-ES heavily suffers from the well-known curse of dimensionality, which makes it impractical to many Large Scale Global Optimization (LSGO) problems. In this paper, a correlation coefficient based grouping (CCG) strategy is proposed to detect the correlations between variables in a simple yet efficient way. Then coupled with a model complexity control (MCC) framework, a new variant of CMA-ES, named MCC-CCG-CMAES, is presented for LSGO problems, which suffers less from curse of dimensionality and significantly reduces the computational cost compared with the standard CMA-ES. To the best of our knowledge, this work is the first attempt at enhancing CMA-ES with the MCC framework rather than the cooperative coevolution (CC) framework. Experimental results on the CEC′2010 large-scale global optimization (LSGO) benchmark functions show that the performance of MCC-CCG-CMAES outperforms the state-of-the-art counterparts.

Correlation coefficient based grouping Covariance matrix adaptation evolution strategy Large scale global optimization Model complex control

SCI ; EI

英语

其他

Science and Technology Innovation Committee Foundation of Shenzhen[JCYJ20180504165652917]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000497252300041

ELSEVIER

20193307303740

Benchmarking ; Global optimization

Mathematics:921 ; Optimization Techniques:921.5

2-s2.0-85070385502

Scopus

1.School of Information Science and TechnologyUniversity of Science and Technology of China,Hefei,230027,China
2.Department of Computer Science and EngineeringSouthern University of Science and Technology,Shenzhen,518055,China

Tong，Xin,Yuan，Bo,Li，Bin. Model complex control CMA-ES[J]. Swarm and Evolutionary Computation,2019,50.

Tong，Xin,Yuan，Bo,&Li，Bin.(2019).Model complex control CMA-ES.Swarm and Evolutionary Computation,50.

Tong，Xin,et al."Model complex control CMA-ES".Swarm and Evolutionary Computation 50(2019).