TY - GEN
T1 - Principled evolutionary algorithm design and the kernel trick
AU - Lane, Fergal
AU - Azad, R. Muhammad Atif
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2016 Copyright held by the owner/author(s).
PY - 2016/7/20
Y1 - 2016/7/20
N2 - We introduce a new approach to the principled design of evolutionary algorithms (EAs) based on kernel methods. We demonstrate how kernel functions, which capture useful problem domain knowledge, can be used to directly construct EA search operators. We test two kernel search operators on a suite of four challenging combinatorial optimization problem domains. These novel kernel search operators exhibit superior performance to some traditional EA search operators.
AB - We introduce a new approach to the principled design of evolutionary algorithms (EAs) based on kernel methods. We demonstrate how kernel functions, which capture useful problem domain knowledge, can be used to directly construct EA search operators. We test two kernel search operators on a suite of four challenging combinatorial optimization problem domains. These novel kernel search operators exhibit superior performance to some traditional EA search operators.
KW - Combinatorial optimization
KW - Evolutionary algorithms
KW - Kernel methods
KW - Search operator design
UR - http://www.scopus.com/inward/record.url?scp=84986253438&partnerID=8YFLogxK
U2 - 10.1145/2908961.2909005
DO - 10.1145/2908961.2909005
M3 - Conference contribution
AN - SCOPUS:84986253438
T3 - GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
SP - 149
EP - 150
BT - GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
A2 - Friedrich, Tobias
PB - Association for Computing Machinery, Inc
T2 - 2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
Y2 - 20 July 2016 through 24 July 2016
ER -