Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
PADO: a new learning architecture for object recognition
Symbolic visual learning
Simultaneous evolution of programs and their control structures
Advances in genetic programming
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Proceedings of the European Conference on Genetic Programming
Linear-Tree GP and Its Comparison with Other GP Structures
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Linear-Graph GP - A New GP Structure
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Strongly typed genetic programming
Evolutionary Computation
Learning recursive functions with object oriented genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
A study of evolutionary multiagent models based on symbiosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary algorithm considering program size: efficient program evolution using grape
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A genetic programming approach to business process mining
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolution of Search Algorithms Using Graph Structured Program Evolution
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Graph structured program evolution with automatically defined nodes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Function sequence genetic programming
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A new, node-focused model for genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Single node genetic programming on problems with side effects
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
EvoGeneSys, a new evolutionary approach to graph generation
Applied Soft Computing
Dynamical genetic programming in xcsf
Evolutionary Computation
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In recent years a lot of Automatic Programming techniques have developed. A typical example of Automatic Programming is Genetic Programming (GP), and various extensions and representations for GP have been proposed so far. However, it seems that more improvements are necessary to obtain complex programs automatically. In this paper we proposed a new method called Graph Structured Program Evolution (GRAPE). The representation of GRAPE is graph structure, therefore it can represent complex programs (e.g. branches and loops) using its graph structure. Each program is constructed as an arbitrary directed graph of nodes and data set. The GRAPE program handles multiple data types using the data set for each type, and the genotype of GRAPE is the form of a linear string of integers. We apply GRAPE to four test problems, factorial, Fibonacci sequence, exponentiation and reversing a list, and demonstrate that the optimum solution in each problem is obtained by the GRAPE system.