TY - GEN
T1 - Investigation of three genotypes for mixed variable evolutionary optimization
AU - Filomeno Coelho, Rajan
AU - Xiao, Manyu
AU - Guglielmetti, Aurore
AU - Herrera, Manuel
AU - Zhang, Weihong
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - While the handling of optimization variables directly expressed by numbers (continuous, discrete, or integer) is abundantly investigated in the literature, the use of nominal variables is generally overlooked, despite its practical interest in plenty of scientific and industrial applications. For example, in civil engineering, the designers of a structure made out of beams might have to select the best cross-section shapes among a list of available geometries (square, circular, rectangular, etc.), which can be modeled by nominal data. Therefore, in the context of singleand multi-objective evolutionary optimization for mixed variables, this study investigates three genetic encodings (binary, real, and real-simplex) for the representation of mixed variables involving both continuous and nominal parameters. The comparison of the genotypes combined with the instances of crossover is performed on six analytical benchmark test functions, as well as on the multi-objective design optimization of a six-storey rigid frame, showing that for mixed variables, real (and to a lesser extent: real-simplex) coding provides the best results, especially when combined with a uniform crossover.
AB - While the handling of optimization variables directly expressed by numbers (continuous, discrete, or integer) is abundantly investigated in the literature, the use of nominal variables is generally overlooked, despite its practical interest in plenty of scientific and industrial applications. For example, in civil engineering, the designers of a structure made out of beams might have to select the best cross-section shapes among a list of available geometries (square, circular, rectangular, etc.), which can be modeled by nominal data. Therefore, in the context of singleand multi-objective evolutionary optimization for mixed variables, this study investigates three genetic encodings (binary, real, and real-simplex) for the representation of mixed variables involving both continuous and nominal parameters. The comparison of the genotypes combined with the instances of crossover is performed on six analytical benchmark test functions, as well as on the multi-objective design optimization of a six-storey rigid frame, showing that for mixed variables, real (and to a lesser extent: real-simplex) coding provides the best results, especially when combined with a uniform crossover.
KW - Categorical variables
KW - Evolutionary algorithms
KW - Genotype
KW - Mixed variables
UR - http://www.scopus.com/inward/record.url?scp=84962910746&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11541-2_20
DO - 10.1007/978-3-319-11541-2_20
M3 - 会议稿件
AN - SCOPUS:84962910746
SN - 9783319115405
T3 - Computational Methods in Applied Sciences
SP - 309
EP - 319
BT - Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences
A2 - Greiner, David
A2 - Galván, Blas
A2 - Winter, Gabriel
A2 - Périaux, Jacques
A2 - Périaux, Jacques
A2 - Gauger, Nicolas
A2 - Giannakoglou, Kyriakos
PB - Springer Science and Business Media B.V.
T2 - 10th International Conference on Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, 2013
Y2 - 7 October 2013 through 9 October 2013
ER -