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
Alternatives in automatic function definition: a comparison of performance
Advances in genetic programming
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Simultaneous evolution of programs and their control structures
Advances in genetic programming
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming and Autoconstructive Evolution with the Push Programming Language
Genetic Programming and Evolvable Machines
Evolving Modules in Genetic Programming by Subtree Encapsulation
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
A Dynamic Lattice to Evolve Hierarchically Shared Subroutines
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
The Push3 execution stack and the evolution of control
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Genetic programming for finite algebras
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Graph structured program evolution with automatically defined nodes
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the performance effects of unbiased module encapsulation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A grammatical genetic programming approach to modularity in genetic algorithms
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Evolving specialisation, altruism, and group-level optimisation using tags
MABS'02 Proceedings of the 3rd international conference on Multi-agent-based simulation II
Undirected training of run transferable libraries
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming
IEEE Transactions on Evolutionary Computation
Tag-based modularity in tree-based genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Comparing methods for module identification in grammatical evolution
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Autoconstructive evolution for structural problems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Automated problem decomposition for the boolean domain with genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Evolving a digital multiplier with the pushgp genetic programming system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Genetic Programming and Emergence
Genetic Programming and Evolvable Machines
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In this paper we present a new technique for evolving modular programs with genetic programming. The technique is based on the use of "tags" that evolving programs may use to label and later to refer to code fragments. Tags may refer inexactly, permitting the labeling and use of code fragments to co-evolve in an incremental way. The technique can be implemented as a minor modification to an existing, general purpose genetic programming system, and it does not require pre-specification of the module architecture of evolved programs. We demonstrate that tag-based modules readily evolve and that this allows problem solving effort to scale well with problem size. We also show that the tag-based module technique is effective even in complex, non-uniform problem environments for which previous techniques perform poorly. We demonstrate the technique in the context of the stack-based genetic programming system PushGP, but we also briefly discuss ways in which it may be used with other kinds of genetic programming systems.