Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
Convergence Rates For The Distribution Of Program Outputs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Sub-tree Swapping Crossover, Allele Diffusion and GP Convergence
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Convergence of program fitness landscapes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
On the limiting distribution of program sizes in tree-based genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Operator equalisation and bloat free GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Crossover, sampling, bloat and the harmful effects of size limits
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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Recent theoretical work has characterised the search bias of GP sub-tree swapping crossover in terms of program length distributions, providing an exact fixed point for trees with internal nodes of identical arity. However, only an approximate model (based on the notion of average arity) for the mixed-arity case has been proposed. This leaves a particularly important gap in our knowledge because multi-arity function sets are commonplace in GP and deep lessons could be learnt from the fixed point. In this paper, we present an accurate theoretical model of program length distributions when mixed-arity function sets are employed. The new model is based on the notion of an arity histogram, a count of the number of primitives of each arity in a program. Empirical support is provided and a discussion of the model is used to place earlier findings into a more general context.