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
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Convergence Rates For The Distribution Of Program Outputs
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
General Schema Theory for Genetic Programming with Subtree-Swapping Crossover
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
General schema theory for genetic programming with subtree-swapping crossover: Part II
Evolutionary Computation
Dynamics of evolutionary robustness
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A quantitative study of neutrality in GP boolean landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Effects of code growth and parsimony pressure on populations in genetic programming
Evolutionary Computation
Convergence of program fitness landscapes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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
On the limiting distribution of program sizes in tree-based genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
The halting probability in von neumann architectures
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Genetic Programming and Evolvable Machines
The Role of Population Size in Rate of Evolution in Genetic Programming
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP
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
Program optimization by random tree sampling
Proceedings of the 11th Annual conference on Genetic and 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
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
Hi-index | 0.00 |
The crossover bias theory for bloat [18] is a recent result which predicts that bloat is caused by the sampling of short, unfit programs. This theory is clear and simple, but it has some weaknesses: (1) it implicitly assumes that the population is large enough to allow sampling of all relevant program sizes (although it does explain what to expect in the many practical cases where this is not true, e.g., because the population is small); (2) it does not explain what is meant by its assumption that short programs are unfit. In this paper we discuss these weaknesses and propose a refined version of the crossover bias theory that clarifies the relationship between bloat and finite populations, and explains what features of the fitness landscape cause bloat to occur. The theory, in particular, predicts that smaller populations will bloat more slowly than larger ones. Additionally, the theory predicts that bloat will only be observed in problems where short programs are less fit than longer ones when looking at samples created by fitness-based importance sampling, i.e. samplings of the search space in which fitter programs have a higher probability of being sampled (e.g., the Metropolis-Hastings method). Experiments with two classical GP benchmarks fully corroborate the theory.