TY - GEN
T1 - A new multiobjective genetic programming for extraction of design information from non-dominated solutions
AU - Tatsukawa, Tomoaki
AU - Nonomura, Taku
AU - Oyama, Akira
AU - Fujii, Kozo
PY - 2013
Y1 - 2013
N2 - We propose a new type of multi-objective genetic programming (MOGP) for multi-objective design exploration (MODE). The characteristic of the new MOGP is the simultaneous symbolic regression to multiple objective functions using correlation coefficients. This methodology is applied to non-dominated solutions of the multi-objective design optimization problem to extract information between objective functions and design parameters. The result of MOGP is symbolic equations that are highly correlated to each objective function through a single GP run. These equations are also highly correlated to several objective functions. The results indicate that the proposed MOGP is capable of finding new design parameters more closely related to the objective functions than the original design parameters. The proposed MOGP is applied to the test problem and the practical design problem to evaluate the capability.
AB - We propose a new type of multi-objective genetic programming (MOGP) for multi-objective design exploration (MODE). The characteristic of the new MOGP is the simultaneous symbolic regression to multiple objective functions using correlation coefficients. This methodology is applied to non-dominated solutions of the multi-objective design optimization problem to extract information between objective functions and design parameters. The result of MOGP is symbolic equations that are highly correlated to each objective function through a single GP run. These equations are also highly correlated to several objective functions. The results indicate that the proposed MOGP is capable of finding new design parameters more closely related to the objective functions than the original design parameters. The proposed MOGP is applied to the test problem and the practical design problem to evaluate the capability.
KW - CFD
KW - Multi-Objective Design Explolation
KW - Multi-Objective Genetic Programming
KW - NSGA-II
UR - http://www.scopus.com/inward/record.url?scp=84875531582&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875531582&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37140-0_40
DO - 10.1007/978-3-642-37140-0_40
M3 - Conference contribution
AN - SCOPUS:84875531582
SN - 9783642371394
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 528
EP - 542
BT - Evolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings
T2 - 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013
Y2 - 19 March 2013 through 22 March 2013
ER -