%0 Conference Paper %1 Yin:2007:pliks %A Yin, Zheng %A Brabazon, Anthony %A O'Sullivan, Conall %A O'Neill, Michael %B 2nd International Symposium Ädvances in Artificial Intelligence and Applications" %C Wisla, Poland %D 2007 %K algorithms, dynamic environments genetic programming, %P 437--446 %T Genetic Programming for Dynamic Environments %U http://www.proceedings2007.imcsit.org/pliks/18.pdf %V 2 %X Genetic Programming (GP) is an automated computational programming methodology which is inspired by the workings of natural evolution techniques. It has been applied to solve complex problems in multiple application domains. This paper investigates the application of a dynamic form of GP in which the probability of crossover and mutation adapts during the GP run. This allows GP to adapt its diversity-generating process during a run in response to feedback from the fitness function. A proof of concept study is then undertaken on the important real-world problem of options pricing. The results indicate that the dynamic form of GP yields better results than are obtained from canonical GP with fixed crossover and mutation rates. The developed method has potential for implementation across a range of dynamic problem environments.

@inproceedings{Yin:2007:pliks, abstract = {Genetic Programming (GP) is an automated computational programming methodology which is inspired by the workings of natural evolution techniques. It has been applied to solve complex problems in multiple application domains. This paper investigates the application of a dynamic form of GP in which the probability of crossover and mutation adapts during the GP run. This allows GP to adapt its diversity-generating process during a run in response to feedback from the fitness function. A proof of concept study is then undertaken on the important real-world problem of options pricing. The results indicate that the dynamic form of GP yields better results than are obtained from canonical GP with fixed crossover and mutation rates. The developed method has potential for implementation across a range of dynamic problem environments.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Wisla, Poland}, author = {Yin, Zheng and Brabazon, Anthony and O'Sullivan, Conall and O'Neill, Michael}, biburl = {https://www.bibsonomy.org/bibtex/2380df1cf97976bb8a19084abd128cd56/brazovayeye}, booktitle = {2nd International Symposium {"}Advances in Artificial Intelligence and Applications{"}}, interhash = {778b440a77cf44cf6f525a4786fc9f9b}, intrahash = {380df1cf97976bb8a19084abd128cd56}, issn = {1896 7094}, keywords = {algorithms, dynamic environments genetic programming,}, month = {October 15-17}, pages = {437--446}, size = {10 pages}, timestamp = {2008-06-19T17:54:55.000+0200}, title = {Genetic Programming for Dynamic Environments}, url = {http://www.proceedings2007.imcsit.org/pliks/18.pdf}, volume = 2, year = 2007 }