TY - GEN
T1 - On crossover success rate in genetic programming with offspring selection
AU - Kronberger, Gabriel
AU - Winkler, Stephan
AU - Affenzeller, Michael
AU - Wagner, Stefan
N1 - Funding Information:
The work described in this paper was done within HEUREKA!, the Josef Ressel centre for heuristic optimization sponsored by the Austrian Research Promotion Agency (FFG).
PY - 2009
Y1 - 2009
N2 - A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are still largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring (success rate) when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators does not improve the performance of the algorithm in terms of best solution quality or efficiency.
AB - A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are still largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring (success rate) when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators does not improve the performance of the algorithm in terms of best solution quality or efficiency.
UR - http://www.scopus.com/inward/record.url?scp=67650697118&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-01181-8_20
DO - 10.1007/978-3-642-01181-8_20
M3 - Conference contribution
SN - 3642011802
SN - 9783642011801
VL - 5481
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 232
EP - 243
BT - Genetic Programming - 12th European Conference, EuroGP 2009, Proceedings
T2 - 12th European Conference on Genetic Programming, EuroGP 2009
Y2 - 15 April 2009 through 17 April 2009
ER -