%0 Journal Article %1 wong:2005:GPEM %A Wong, Man Leung %D 2005 %J Genetic Programming and Evolvable Machines %K algorithms, based genetic grammar grammars, logic programming, programs recursive %N 4 %P 421--455 %R doi:10.1007/s10710-005-4805-8 %T Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming %U http://cptra.ln.edu.hk/~mlwong/journal/gpem2005.pdf %V 6 %X Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniques are incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.

@article{wong:2005:GPEM, abstract = {Genetic programming (GP) extends traditional genetic algorithms to automatically induce computer programs. GP has been applied in a wide range of applications such as software re-engineering, electrical circuits synthesis, knowledge engineering, and data mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolve recursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Because recursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solve larger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is very difficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniques are incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have been performed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Wong, Man Leung}, biburl = {https://www.bibsonomy.org/bibtex/2c4d592ae10e03c8c4ce9cb8a88fd651d/brazovayeye}, doi = {doi:10.1007/s10710-005-4805-8}, interhash = {67b37c4ed19ae01577c6ebbee8a06fc5}, intrahash = {c4d592ae10e03c8c4ce9cb8a88fd651d}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {algorithms, based genetic grammar grammars, logic programming, programs recursive}, month = {December}, notes = {An erratum to this article is available at http://dx.doi.org/10.1007/s10710-006-7455-6 : 'The publisher apologizes for an error that occurred in the above mentioned article. The error appears in the printed version, as well as in the html and pdf version online. Man Leung Wong is the sole author of this article' 'contains logic gaols' genetic operations performed on derivation tree, biases updated during run. Final grammar better (easier to evolve solutions from) than initial one, self adaption. non-terminating programs direct selection of crossover points. 11-Mux. General solution to even-n-parity. p452 GP outperformed random search.}, number = 4, pages = {421--455}, size = {35 pages}, timestamp = {2008-06-19T17:54:24.000+0200}, title = {Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming}, url = {http://cptra.ln.edu.hk/~mlwong/journal/gpem2005.pdf}, volume = 6, year = 2005 }