TY - CHAP
T1 - Multi-Population Genetic Programming with Data Migration for Symbolic Regression
AU - Kommenda, Michael
AU - Affenzeller, Michael
AU - Kronberger, Gabriel
AU - Burlacu, Bogdan
AU - Winkler, Stephan
PY - 2015
Y1 - 2015
N2 - In this contribution we study the effects of multi-population genetic programming for symbolic regression problems. In addition to the parallel evolution of several subpopulations according to an island model with unidirectional ring migration, the data partitions, on which the individuals are evolved, differ for every island and are adapted during algorithm execution. These modifications are intended to increase the generalization capabilities of the solutions and to maintain the genetic diversity. The effects of multiple populations as well as the used data migration strategy are compared to standard genetic programming algorithms on several symbolic regression benchmark problems.
AB - In this contribution we study the effects of multi-population genetic programming for symbolic regression problems. In addition to the parallel evolution of several subpopulations according to an island model with unidirectional ring migration, the data partitions, on which the individuals are evolved, differ for every island and are adapted during algorithm execution. These modifications are intended to increase the generalization capabilities of the solutions and to maintain the genetic diversity. The effects of multiple populations as well as the used data migration strategy are compared to standard genetic programming algorithms on several symbolic regression benchmark problems.
UR - http://www.scopus.com/inward/record.url?scp=84924942610&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-15720-7_6
DO - 10.1007/978-3-319-15720-7_6
M3 - Chapter
SN - 1860-949X
VL - 595
T3 - Studies in Computational Intelligence
SP - 75
EP - 87
BT - Computational Intelligence and Efficiency in Engineering Systems
PB - Springer
ER -