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
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Modification point depth and genome growth in genetic programming
Evolutionary Computation
Genetic programming for human oral bioavailability of drugs
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic programming for computational pharmacokinetics in drug discovery and development
Genetic Programming and Evolvable Machines
The impact of population size on code growth in GP: analysis and empirical validation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines
Generality versus size in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Fitness distance correlation in structural mutation genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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
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
Measuring bloat, overfitting and functional complexity in genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Reassembling operator equalisation: a secret revealed
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Bloat control in genetic programming with a histogram-based accept-reject method
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Overfitting detection and adaptive covariant parsimony pressure for symbolic regression
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A quantitative study of learning and generalization in genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Reassembling operator equalisation: a secret revealed
ACM SIGEVOlution
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
Spatial co-evolution: quicker, fitter and less bloated
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Where should we stop? an investigation on early stopping for GP learning
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Operator equalisation was recently proposed as a new bloat control technique for genetic programming. By controlling the distribution of program lengths inside the population, it can bias the search towards smaller or larger programs. In this paper we propose a new implementation of operator equalisation and compare it to a previous version, using a hard real-world regression problem where bloat and overfitting are major issues. The results show that both implementations of operator equalisation are completely bloat-free, producing smaller individuals than standard genetic programming, without compromising the generalization ability. We also show that the new implementation of operator equalisation is more efficient and exhibits a more predictable and reliable behavior than the previous version. We advance some arguable ideas regarding the relationship between bloat and overfitting, and support them with our results.