TY - GEN
T1 - Cooperative co-evolution using LSHADE with restarts for the CEC15 benchmarks
AU - El-Abd, Mohammed
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - In this paper, we test the performance of an LSHADE Cooperative Co-evolutionary (CC) algorithm using the CEC15 benchmarks. First, we apply the recently proposed Global Differential Grouping (GDG) to learn the underlying interdependencies of the problem variables. GDG divides both separable and non-separable variables among multiple sets. Second, the method adopts the LSHADE algorithm within the CC framework to simultaneously optimize the identified groups. Results are reported for all required problem sizes.
AB - In this paper, we test the performance of an LSHADE Cooperative Co-evolutionary (CC) algorithm using the CEC15 benchmarks. First, we apply the recently proposed Global Differential Grouping (GDG) to learn the underlying interdependencies of the problem variables. GDG divides both separable and non-separable variables among multiple sets. Second, the method adopts the LSHADE algorithm within the CC framework to simultaneously optimize the identified groups. Results are reported for all required problem sizes.
UR - http://www.scopus.com/inward/record.url?scp=85008255924&partnerID=8YFLogxK
U2 - 10.1109/CEC.2016.7744406
DO - 10.1109/CEC.2016.7744406
M3 - Conference contribution
T3 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
SP - 4810
EP - 4814
BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Y2 - 24 July 2016 through 29 July 2016
ER -