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
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on 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
Genetic Programming and Evolvable Machines
Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of constant creation techniques on the binomial-3 problem with grammatical evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Bloat control operators and diversity in genetic programming: A comparative study
Evolutionary Computation
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Reassembling operator equalisation: a secret revealed
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Reassembling operator equalisation: a secret revealed
ACM SIGEVOlution
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Sub-tree swapping crossover and arity histogram distributions
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
A relaxed approach to simplification in genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
Bloat free genetic programming: application to human oral bioavailability prediction
International Journal of Data Mining and Bioinformatics
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Recent research [9,2] has enabled the accurate prediction of the limiting distribution of tree sizes for Genetic Programming with standard sub-tree swapping crossover when GP is applied to a flat fitness landscape. In that work, however, tree sizes are measured in terms of number of internal nodes. While the relationship between internal nodes and length is one-to-one for the case of a-ary trees, it is much more complex in the case of mixed arities. So, practically the length bias of subtree crossover remains unknown. This paper starts to fill this theoretical gap, by providing accurate estimates of the limiting distribution of lengths approached by tree-based GP with standard crossover in the absence of selection pressure. The resulting models confirm that short programs can be expected to be heavily resampled. Empirical validation shows that this is indeed the case. We also study empirically how the situation is modified by the application of program length limits. Surprisingly, the introduction of such limits further exacerbates the effect. However, this has more profound consequences than one might imagine at first. We analyse these consequences and predict that, in the presence of fitness, size limits may initially speed up bloat, almost completely defeating their original purpose (combating bloat). Indeed, experiments confirm that this is the case for the first 10 or 15 generations. This leads us to suggest a better way of using size limits. Finally, this paper proposes a novel technique to counteract bloat, sampling parsimony, the application of a penalty to resampling.