Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Proceedings of the European Conference on Genetic Programming
Linear Genetic Programming
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
Genetic programming on GPUs for image processing
International Journal of High Performance Systems Architecture
Developments in Cartesian Genetic Programming: self-modifying CGP
Genetic Programming and Evolvable Machines
Analyzing the credit default swap market using Cartesian genetic programming
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
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Two prominent genetic programming approaches are the graph-based Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP). Recently, a formal algorithm for constructing a directed acyclic graph (DAG) from a classical LGP instruction sequence has been established. Given graph-based LGP and traditional CGP, this paper investigates the similarities and differences between the two implementations, and establishes that the significant difference between them is each algorithm's means of restricting interconnectivity of nodes. The work then goes on to compare the performance of two representations each (with varied connectivity) of LGP and CGP to a directed cyclic graph (DCG) GP with no connectivity restrictions on a medical classification and regression benchmark.