TY - GEN
T1 - On the success rate of crossover operators for genetic programming with offspring selection
AU - Kronberger, Gabriel
AU - Winkler, Stephan
AU - Affenzeller, Michael
AU - Beham, Andreas
AU - Wagner, Stefan
N1 - Funding Information:
The work described in this paper was done within HEUREKA!, the Josef Ressel center for heuristic optimization sponsored by the Austrian Research Promotion Agency (FFG).
PY - 2009
Y1 - 2009
N2 - Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and time-series forecasting problems. 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 largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring 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 can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.
AB - Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and time-series forecasting problems. 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 largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring 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 can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.
UR - http://www.scopus.com/inward/record.url?scp=78651231745&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04772-5_102
DO - 10.1007/978-3-642-04772-5_102
M3 - Conference contribution
SN - 3642047718
SN - 9783642047718
VL - 5717
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 793
EP - 800
BT - Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers
T2 - 12th International Conference on Computer Aided Systems Theory, EUROCAST 2009
Y2 - 15 February 2009 through 20 February 2009
ER -