TY - GEN
T1 - Performance evaluation of the Genetic Landscape Evolution (GLE) model with respect to crossover schemes
AU - Kim, Jongchun
AU - Paik, Kyungrock
PY - 2016
Y1 - 2016
N2 - We investigate performance of the Genetic Landscape Evolution (GLE) model by changing number of crossover points, which controls spatial cohesiveness of topological information in generated offspring. Simulation results show that 1) GLE performance is insensitive to the number of crossover points, implying that the spatial cohesiveness does not significantly affect efficiency to find better solution sets; and 2) the method to generate randomness in GLE is a significant element for its performance.
AB - We investigate performance of the Genetic Landscape Evolution (GLE) model by changing number of crossover points, which controls spatial cohesiveness of topological information in generated offspring. Simulation results show that 1) GLE performance is insensitive to the number of crossover points, implying that the spatial cohesiveness does not significantly affect efficiency to find better solution sets; and 2) the method to generate randomness in GLE is a significant element for its performance.
KW - 2-D crossover
KW - Genetic algorithm
KW - Genetic landscape evolution
KW - Optimal channel network
UR - http://www.scopus.com/inward/record.url?scp=84946739426&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946739426&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-47926-1_36
DO - 10.1007/978-3-662-47926-1_36
M3 - Conference contribution
AN - SCOPUS:84946739426
SN - 9783662479254
VL - 382
T3 - Advances in Intelligent Systems and Computing
SP - 377
EP - 383
BT - Advances in Intelligent Systems and Computing
PB - Springer Verlag
T2 - 2nd International Conference on Harmony Search Algorithm, ICHSA 2015
Y2 - 19 August 2015 through 21 August 2015
ER -