TY - GEN
T1 - Using gradient-free local search within MOEAs for the treatment of constrained MOPs
AU - Uribe, Lourdes
AU - Lara, Adriana
AU - Deb, Kalyanmoy
AU - Schütze, Oliver
N1 - Publisher Copyright:
© 2020 Owner/Author.
PY - 2020/7/8
Y1 - 2020/7/8
N2 - Evolutionary algorithms are widely used for the treatment of multi-objective optimization problems due to their global nature, robustness, and their minimal assumptions on the model. In turn, it is widely accepted that they still need quite a few resources in order to obtain a suitable finite size approximation of the Pareto set/front of a given problem. In this work, we make a first effort to study the effect of computing multi-objective descent directions for local search within evolutionary algorithms without explicitly using gradient information. Numerical results on some bi-objective problems show the benefit of the chosen approach.
AB - Evolutionary algorithms are widely used for the treatment of multi-objective optimization problems due to their global nature, robustness, and their minimal assumptions on the model. In turn, it is widely accepted that they still need quite a few resources in order to obtain a suitable finite size approximation of the Pareto set/front of a given problem. In this work, we make a first effort to study the effect of computing multi-objective descent directions for local search within evolutionary algorithms without explicitly using gradient information. Numerical results on some bi-objective problems show the benefit of the chosen approach.
KW - Constraint handling
KW - Descent direction
KW - Evolutionary computation
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85089739406&partnerID=8YFLogxK
U2 - 10.1145/3377929.3390028
DO - 10.1145/3377929.3390028
M3 - Contribución a la conferencia
AN - SCOPUS:85089739406
T3 - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
SP - 177
EP - 178
BT - GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2020 Genetic and Evolutionary Computation Conference, GECCO 2020
Y2 - 8 July 2020 through 12 July 2020
ER -