Uniform crossover in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Advances in genetic programming
An introduction to the analysis of algorithms
An introduction to the analysis of algorithms
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
The evolution of size and shape
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Repeated patterns in tree genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
On Crossover Success Rate in Genetic Programming with Offspring Selection
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis
Genetic Programming and Evolvable Machines
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Genetic Programming and Emergence
Genetic Programming and Evolvable Machines
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Evolved genetic programming trees contain many repeated code fragments. Size fair crossover limits bloat in automatic programming, preventing the evolution of recurring motifs. We examine these complex properties in detail using depth vs. size Catalan binary tree shape plots, subgraph and subtree matching, information entropy, sensitivity analysis, syntactic and semantic fitness correlations. Programs evolve in a self-similar fashion, akin to fractal random trees, with diffuse introns. Data mining frequent patterns reveals that as software is progressively improved a large proportion of it is exactly repeated subtrees as well as exactly repeated subgraphs. We relate this emergent phenomenon to building blocks in GP and suggest GP works by jumbling subtrees which already have high fitness on the whole problem to give incremental improvements and create complete solutions with multiple identical components of different importance.