TY - GEN
T1 - A self-guided genetic algorithm with dominance properties for single machine scheduling problems
AU - Chen, Shi
AU - Chang, Pei Chann
AU - Chen, Min Chih
AU - Chen, Yuh Min
PY - 2009
Y1 - 2009
N2 - In this study we integrate a Self-guided Genetic Algorithm with dominance properties (DPs) which is named DPSelf- guided GA. Self-guided GA [8] is belonged to the category of evolutionary algorithms based on probabilistic models (EAPM) and it is effective and efficient in solving the scheduling problems. In order to further enhance the performance of this algorithm, it is thus integrated with DPs because DPs is a mathematical algorithm which is able to generate good solutions quickly. As a result, the solutions generated by DPs will be applied as the initial population of Self-guided GA instead of using the randomly generated initial solutions. When we conducted an extensive experiments to validate DP-Self-guided GA, it is statistically significant when we compared it with existing algorithms in the literature. As a result, the implication of this approach is a good heuristic which may further improve the performance of an EAPM algorithm.
AB - In this study we integrate a Self-guided Genetic Algorithm with dominance properties (DPs) which is named DPSelf- guided GA. Self-guided GA [8] is belonged to the category of evolutionary algorithms based on probabilistic models (EAPM) and it is effective and efficient in solving the scheduling problems. In order to further enhance the performance of this algorithm, it is thus integrated with DPs because DPs is a mathematical algorithm which is able to generate good solutions quickly. As a result, the solutions generated by DPs will be applied as the initial population of Self-guided GA instead of using the randomly generated initial solutions. When we conducted an extensive experiments to validate DP-Self-guided GA, it is statistically significant when we compared it with existing algorithms in the literature. As a result, the implication of this approach is a good heuristic which may further improve the performance of an EAPM algorithm.
UR - http://www.scopus.com/inward/record.url?scp=67650507151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650507151&partnerID=8YFLogxK
U2 - 10.1109/SCIS.2009.4927018
DO - 10.1109/SCIS.2009.4927018
M3 - Conference contribution
AN - SCOPUS:67650507151
SN - 9781424427574
T3 - 2009 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2009 - Proceedings
SP - 76
EP - 83
BT - 2009 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2009 - Proceedings
T2 - 2009 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2009
Y2 - 30 March 2009 through 2 April 2009
ER -