Hybrid Sampling Evolution Strategy for Solving Single Objective Bound Constrained Problems

Zhang, Geng; Shi, Yuhui

10.1109/CEC.2018.8477908

2018

978-1-5090-6018-4

2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)

765-771

8-13 July 2018

Rio de Janeiro, Brazil

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

This paper proposes an evolution strategy (ES) algorithm called hybrid sampling-evolution strategy (HS-ES) that combines the covariance matrix adaptation-evolution strategy (CMA-ES) and univariate sampling method. In spite that the univariate sampling has been widely thought as a method only to separable problems, the analysis and experimental tests show that it is actually very effective for solving multimodal nonseparable problems. As the univariate sampling is a complementary algorithm to the CMA-ES which has obvious advantages for solving unimodal nonseparable problems, the proposed HS-ES tries to take advantages of these two algorithms to improve its searching performance. Experimental results on CEC-2018 demonstrate the effectiveness of the proposed HS-ES.

univariate sampling evolution strategy multi modal nonseparable problems

第一 ; 通讯

英语

CPCI-S ; EI

Science and Technology Innovation Committee Foundation of Shenzhen[ZDSYS201703031748284]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic

WOS:000451175500099

20184606066472

Covariance matrix ; Optimization ; Problem solving

Mathematics:921 ; Optimization Techniques:921.5

Web of Science

Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen Key Lab Computat Intelligence, Shenzhen, Peoples R China

Zhang, Geng,Shi, Yuhui. Hybrid Sampling Evolution Strategy for Solving Single Objective Bound Constrained Problems[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2018:765-771.