TY - GEN
T1 - Multivariate cauchy EDA optimisation
AU - Sanyang, Momodou L.
AU - Kaban, Ata
PY - 2014
Y1 - 2014
N2 - We consider Black-Box continuous optimization by Estimation of Distribution Algorithms (EDA). In continuous EDA, the multivariate Gaussian distribution is widely used as a search operator, and it has the well-known advantage of modelling the correlation structure of the search variables, which univariate EDA lacks. However, the Gaussian distribution as a search operator is prone to premature convergence when the population is far from the optimum. Recent work suggests that replacing the univariate Gaussian with a univariate Cauchy distribution in EDA holds promise in alleviating this problem because it is able to make larger jumps in the search space due to the Cauchy distribution's heavy tails. In this paper, we propose the use of a multivariate Cauchy distribution to blend together the advantages of multivariate modelling with the ability of escaping early convergence to efficiently explore the search space. Experiments on 16 benchmark functions demonstrate the superiority of multivariate Cauchy EDA against univariate Cauchy EDA, and its advantages against multivariate Gaussian EDA when the population lies far from the optimum.
AB - We consider Black-Box continuous optimization by Estimation of Distribution Algorithms (EDA). In continuous EDA, the multivariate Gaussian distribution is widely used as a search operator, and it has the well-known advantage of modelling the correlation structure of the search variables, which univariate EDA lacks. However, the Gaussian distribution as a search operator is prone to premature convergence when the population is far from the optimum. Recent work suggests that replacing the univariate Gaussian with a univariate Cauchy distribution in EDA holds promise in alleviating this problem because it is able to make larger jumps in the search space due to the Cauchy distribution's heavy tails. In this paper, we propose the use of a multivariate Cauchy distribution to blend together the advantages of multivariate modelling with the ability of escaping early convergence to efficiently explore the search space. Experiments on 16 benchmark functions demonstrate the superiority of multivariate Cauchy EDA against univariate Cauchy EDA, and its advantages against multivariate Gaussian EDA when the population lies far from the optimum.
KW - Black-box Optimization
KW - Estimation of Distribution Algorithm
KW - Multivariate Cauchy Distribution
KW - Multivariate Gaussian distribution
UR - http://www.scopus.com/inward/record.url?scp=84906334006&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10840-7_54
DO - 10.1007/978-3-319-10840-7_54
M3 - Conference contribution
SN - 9783319108391
VL - 8669 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 449
EP - 456
BT - Intelligent Data Engineering and Automated Learning
A2 - Corchado, Emilio
A2 - Lozano , José A.
A2 - Quintián , Héctor
A2 - Yin, Hujun
PB - Springer
T2 - 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014
Y2 - 10 September 2014 through 12 September 2014
ER -