TY - GEN
T1 - Make fast evolutionary programming robust by search step control
AU - LIU, Yong
AU - YAO, Xin
PY - 2006
Y1 - 2006
N2 - It has been found that both evolutionary programming (EP) and fast EP (FEP) could get stuck in local optima on some test functions. Although a number of methods have been developed to solve this problem, nearly all have focused on how to adjust search step sizes. This paper shows that it is not enough to change the step sizes alone. Besides step control, the shape of search space should be changed so that the search could be driven to other unexplored regions without getting stuck in the local optima. A two-level FEP with deletion is proposed in this paper to make FEP robust on finding better solutions in function optimisation. A coarse-grained search in the upper level could lead FEP to generate a diverse population, while a fine-grained search in the lower level would help FEP quickly find a local optimum in a region. After FEP could not make any progress after falling in a local optimum, deletion would be applied to change the search space so that FEP could start a new fine-grained search from the points generated by the coarse-grained search. © Springer-Verlag Berlin Heidelberg 2006.
AB - It has been found that both evolutionary programming (EP) and fast EP (FEP) could get stuck in local optima on some test functions. Although a number of methods have been developed to solve this problem, nearly all have focused on how to adjust search step sizes. This paper shows that it is not enough to change the step sizes alone. Besides step control, the shape of search space should be changed so that the search could be driven to other unexplored regions without getting stuck in the local optima. A two-level FEP with deletion is proposed in this paper to make FEP robust on finding better solutions in function optimisation. A coarse-grained search in the upper level could lead FEP to generate a diverse population, while a fine-grained search in the lower level would help FEP quickly find a local optimum in a region. After FEP could not make any progress after falling in a local optimum, deletion would be applied to change the search space so that FEP could start a new fine-grained search from the points generated by the coarse-grained search. © Springer-Verlag Berlin Heidelberg 2006.
UR - http://www.scopus.com/inward/record.url?scp=33750377776&partnerID=8YFLogxK
U2 - 10.1007/11881070_107
DO - 10.1007/11881070_107
M3 - Conference paper (refereed)
SN - 9783540459019
T3 - Lecture Notes in Computer Science
SP - 806
EP - 815
BT - Advances in Natural Computation : Second International Conference, ICNC 2006, Xi'an, China, September 24-28, 2006, Proceedings, Part I
A2 - JIAO, Licheng
A2 - WANG, Lipo
A2 - GAO, Xin-bo
A2 - LIU, Jing
A2 - WU, Feng
PB - Springer Berlin Heidelberg
T2 - 2nd International Conference on Natural Computation, ICNC 2006
Y2 - 24 September 2006 through 28 September 2006
ER -