%0 Conference Paper %1 1277288 %A Schmidt, Michael %A Lipson, Hod %B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation %C London %D 2007 %E Thierens, Dirk %E Beyer, Hans-Georg %E Bongard, Josh %E Branke, Jurgen %E Clark, John Andrew %E Cliff, Dave %E Congdon, Clare Bates %E Deb, Kalyanmoy %E Doerr, Benjamin %E Kovacs, Tim %E Kumar, Sanjeev %E Miller, Julian F. %E Moore, Jason %E Neumann, Frank %E Pelikan, Martin %E Poli, Riccardo %E Sastry, Kumara %E Stanley, Kenneth Owen %E Stutzle, Thomas %E Watson, Richard A %E Wegener, Ingo %I ACM Press %K algorithms, expression genetic graphs, programming, regression symbolic trees, %P 1674--1679 %T Comparison of tree and graph encodings as function of problem complexity %U http://doi.acm.org/10.1145/1276958.1277288 %V 2 %X In this paper, we analyse two general-purpose encoding types, trees and graphs systematically, focusing on trends over increasingly complex problems. Tree and graph encodings are similar in application but offer distinct advantages and disadvantages in genetic programming. We describe two implementations and discuss their evolvability. We then compare performance using symbolic regression on hundreds of random nonlinear target functions of both 1-dimensional and 8-dimensional cases. Results show the graph encoding has less bias for bloating solutions but is slower to converge and deleterious crossovers are more frequent. The graph encoding however is found to have computational benefits, suggesting it to be an advantageous trade-off between regression performance and computational effort.

@inproceedings{1277288, abstract = {In this paper, we analyse two general-purpose encoding types, trees and graphs systematically, focusing on trends over increasingly complex problems. Tree and graph encodings are similar in application but offer distinct advantages and disadvantages in genetic programming. We describe two implementations and discuss their evolvability. We then compare performance using symbolic regression on hundreds of random nonlinear target functions of both 1-dimensional and 8-dimensional cases. Results show the graph encoding has less bias for bloating solutions but is slower to converge and deleterious crossovers are more frequent. The graph encoding however is found to have computational benefits, suggesting it to be an advantageous trade-off between regression performance and computational effort.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {London}, author = {Schmidt, Michael and Lipson, Hod}, biburl = {https://www.bibsonomy.org/bibtex/2dec1552b5d1662eb63c47020eceb88ba/brazovayeye}, booktitle = {GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation}, editor = {Thierens, Dirk and Beyer, Hans-Georg and Bongard, Josh and Branke, Jurgen and Clark, John Andrew and Cliff, Dave and Congdon, Clare Bates and Deb, Kalyanmoy and Doerr, Benjamin and Kovacs, Tim and Kumar, Sanjeev and Miller, Julian F. and Moore, Jason and Neumann, Frank and Pelikan, Martin and Poli, Riccardo and Sastry, Kumara and Stanley, Kenneth Owen and Stutzle, Thomas and Watson, Richard A and Wegener, Ingo}, interhash = {c15a19fe641cf8987eea3338064cc46b}, intrahash = {dec1552b5d1662eb63c47020eceb88ba}, isbn13 = {978-1-59593-697-4}, keywords = {algorithms, expression genetic graphs, programming, regression symbolic trees,}, month = {7-11 July}, notes = {GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {1674--1679}, publisher = {ACM Press}, publisher_address = {New York, NY, USA}, timestamp = {2008-06-19T17:51:07.000+0200}, title = {Comparison of tree and graph encodings as function of problem complexity}, url = {http://doi.acm.org/10.1145/1276958.1277288}, volume = 2, year = 2007 }