Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
How to build a Beowulf: a guide to the implementation and application of PC clusters
How to build a Beowulf: a guide to the implementation and application of PC clusters
The evolution of size and shape
Advances in genetic programming
Building Linux Clusters with Cdrom
Building Linux Clusters with Cdrom
What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming
Genetic Programming and Evolvable Machines
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Exons and Code Growth in Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Introns in Nature and in Simulated Structure Evolution
Biocomputing and emergent computation: Proceedings of BCEC97
Code growth in genetic programming
Code growth in genetic programming
Code growth, explicitly defined introns, and alternative selection schemes
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Multi-Objective Methods for Tree Size Control
Genetic Programming and Evolvable Machines
Problem Difficulty and Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
Exploiting disruption aversion to control code bloat
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Resource-limited genetic programming: the dynamic approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multi-chromosomal genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Resilient Individuals Improve Evolutionary Search
Artificial Life
Growth of self-canceling code in evolutionary systems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using convex hulls to represent classifier conditions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Neutral offspring controlling operators in genetic programming
Pattern Recognition
Using Numerical Simplification to Control Bloat in Genetic Programming
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Genetic Programming and Evolvable Machines
Program optimization by random tree sampling
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
IEEE Transactions on Evolutionary Computation
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Semantically driven mutation in genetic programming
CEC'09 Proceedings of the Eleventh conference on Congress on 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
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Neutral variations cause bloat in linear GP
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Multi Niche parallel GP with a junk-code migration model
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
The root causes of code growth in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Implicitly controlling bloat in genetic programming
IEEE Transactions on Evolutionary Computation
DepthLimited crossover in GP for classifier evolution
Computers in Human Behavior
Size control with maximum homologous crossover
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Context-Based repeated sequences in linear genetic programming
EuroGP'05 Proceedings of the 8th 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
Controlling bloat through parsimonious elitist replacement and spatial structure
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Robustness and evolvability of recombination in linear genetic programming
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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This research examines the cause of code growth (bloat) in genetic programming (GP). Currently there are three hypothesized causes of code growth in GP: protection, drift, and removal bias. We show that single node mutations increase code growth in evolving programs. This is strong evidence that the protective hypothesis is correct. We also show a negative correlation between the size of the branch removed during crossover and the resulting change in fitness, but a much weaker correlation for added branches. These results support the removal bias hypothesis, but seem to refute the drift hypothesis. Our results also suggest that there are serious disadvantages to the tree structured programs commonly evolved with GP, because the nodes near the root are effectively fixed in the very early generations.