TY - GEN
T1 - Using gradient information for multi-objective problems in the evolutionary context
AU - Lara, Adriana
AU - Coello, Carlos A.Coello
AU - Schuetze, Oliver
PY - 2010
Y1 - 2010
N2 - The goal of this research is to study the incorporation of gradient-based information when designing Multi-objective Evolutionary Algorithms (MOEAs). We analyze the benefits, and challenges, of using these well developed mathematical programming techniques in order to get hybrid MOEAs. Since we expect the new hybrid algorithms to search effectively and more efficiently than currently available MOEAs, a deeper study of the balance between the computational cost and the benefits of this coupling is highly necessary.
AB - The goal of this research is to study the incorporation of gradient-based information when designing Multi-objective Evolutionary Algorithms (MOEAs). We analyze the benefits, and challenges, of using these well developed mathematical programming techniques in order to get hybrid MOEAs. Since we expect the new hybrid algorithms to search effectively and more efficiently than currently available MOEAs, a deeper study of the balance between the computational cost and the benefits of this coupling is highly necessary.
KW - Gradient-based memetic algorithms
KW - Multi-objective descent directions
UR - http://www.scopus.com/inward/record.url?scp=77955967900&partnerID=8YFLogxK
U2 - 10.1145/1830761.1830847
DO - 10.1145/1830761.1830847
M3 - Contribución a la conferencia
AN - SCOPUS:77955967900
SN - 9781450300735
T3 - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
SP - 2011
EP - 2014
BT - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
T2 - 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Y2 - 7 July 2010 through 11 July 2010
ER -