Foundations of genetic programming
Foundations of genetic programming
Evolving Modules in Genetic Programming by Subtree Encapsulation
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Evaluating GP schema in context
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (Vienna Series in Theoretical Biology)
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Tracer spectrum: a visualisation method for distributed evolutionary computation
Genetic Programming and Evolvable Machines
A non-destructive grammar modification approach to modularity in grammatical evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
IEEE Transactions on Evolutionary Computation
Pattern-guided genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Genetic programming: where meaning emerges from program code
Genetic Programming and Evolvable Machines
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We investigate semantic properties of linear programs, both internally, by analyzing the memory states they produce during execution, and externally, by inspecting program outcomes. The main concept of the formalism we propose is program trace, which reflects the behavior of program in semantic space. It allows us to characterize programming tasks in terms of traces of programs that solve them, and to propose certain measures that reveal their properties. We are primarily interested in measures that quantitatively characterize functional (semantic, behavioral) modularity of programming tasks. The experiments conducted on large samples of linear programs written in Push demonstrate that semantic structure varies from task to task, and reveal patterns of different forms of modularity. In particular, we identify interesting relationships between task modularity, task complexity, and program length, and conclude that a great share of programming tasks are modular.