Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot
Genetic Programming and Evolvable Machines
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving encapsulated programs as shared grammars
Genetic Programming and Evolvable Machines
Self modifying cartesian genetic programming: parity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
SMCGP2: self modifying cartesian genetic programming in two dimensions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A self-scaling instruction generator using cartesian genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
The Regulatory Network Computational Device
Genetic Programming and Evolvable Machines
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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.