TY - JOUR
T1 - Improving evolutionary optimization with metamodel-based operators
AU - Tenne, Yoel
N1 - Publisher Copyright:
© 2023 AIP Publishing LLC.
PY - 2023
Y1 - 2023
N2 - Simulation-driven optimization problems often require large computational resources and as such are often solved with algorithms which rely on surrogates, namely computationally cheaper mathematical approximations of the simulation. A common approach is to use a surrogate in conjunction with an evolutionary algorithm to seek an optimum based on the surrogate's predictions. In this setup the mechanics of the evolutionary operators are unrelated to the surrogate and do not benefit from the information it accumulates during the search. As such this paper proposes new EA operators in which surrogates are intrinsically incorporated. The proposed recombination operator combines local surrogates with an SQP search and the mutation operator uses a global surrogate based on nearest-neighbour distances. Performance analysis based on well-established test functions shows the effectiveness of the proposed implementations.
AB - Simulation-driven optimization problems often require large computational resources and as such are often solved with algorithms which rely on surrogates, namely computationally cheaper mathematical approximations of the simulation. A common approach is to use a surrogate in conjunction with an evolutionary algorithm to seek an optimum based on the surrogate's predictions. In this setup the mechanics of the evolutionary operators are unrelated to the surrogate and do not benefit from the information it accumulates during the search. As such this paper proposes new EA operators in which surrogates are intrinsically incorporated. The proposed recombination operator combines local surrogates with an SQP search and the mutation operator uses a global surrogate based on nearest-neighbour distances. Performance analysis based on well-established test functions shows the effectiveness of the proposed implementations.
UR - http://www.scopus.com/inward/record.url?scp=85176766716&partnerID=8YFLogxK
U2 - 10.1063/5.0164075
DO - 10.1063/5.0164075
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.conferencearticle???
AN - SCOPUS:85176766716
SN - 0094-243X
VL - 2872
JO - AIP Conference Proceedings
JF - AIP Conference Proceedings
IS - 1
M1 - 120093-1
T2 - 11th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2022
Y2 - 5 September 2022 through 8 September 2022
ER -