%0 Journal Article %1 WongMu05 %A Wong, Man %A Mun, Tuen %D 2005 %J Genetic Programming and Evolvable Machines %K enumerative_ip gbgp gp induction inductive_programming program_evolution program_synthesis %N 4 %P 421--455 %T Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming %U http://dx.doi.org/10.1007/s10710-005-4805-8 %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, anddata mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolverecursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Becauserecursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solvelarger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is verydifficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniquesare incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have beenperformed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.

@article{WongMu05, 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, anddata mining. One of the most important and challenging research areas in GP is the investigation of ways to successfully evolverecursive programs. A recursive program is one that calls itself either directly or indirectly through other programs. Becauserecursions lead to compact and general programs and provide a mechanism for reusing program code, they facilitate GP to solvelarger and more complicated problems. Nevertheless, it is commonly agreed that the recursive program learning problem is verydifficult for GP. In this paper, we propose techniques to tackle the difficulties in learning recursive programs. The techniquesare incorporated into an adaptive Grammar Based Genetic Programming system (adaptive GBGP). A number of experiments have beenperformed to demonstrate that the system improves the effectiveness and efficiency in evolving recursive programs.}, added-at = {2008-11-20T16:26:43.000+0100}, author = {Wong, Man and Mun, Tuen}, biburl = {https://www.bibsonomy.org/bibtex/2786207dd7ac09ea8fcf51c1cda53776e/emanuel}, description = {SpringerLink - Zeitschriftenbeitrag}, interhash = {1a6c70991c4da87b5b7dadc6aeabbdf4}, intrahash = {786207dd7ac09ea8fcf51c1cda53776e}, journal = {Genetic Programming and Evolvable Machines}, keywords = {enumerative_ip gbgp gp induction inductive_programming program_evolution program_synthesis}, number = 4, pages = {421--455}, timestamp = {2008-11-20T16:26:43.000+0100}, title = {Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming}, url = {http://dx.doi.org/10.1007/s10710-005-4805-8}, volume = 6, year = 2005 }