Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The evolution of mental models
Advances in genetic programming
An Analysis of the Causes of Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
On Decentralizing Selection Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Algebraic simplification of GP programs during evolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Evolutionary consequences of coevolving targets
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The identification and exploitation of dormancy in genetic programming
Genetic Programming and Evolvable Machines
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
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Implicitly controlling bloat in genetic programming
IEEE Transactions on Evolutionary Computation
Genetic programming needs better benchmarks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The concept of bloat -- the increase of program size without a corresponding increase in fitness -- presents a significant drawback to the application of genetic programming. One approach to controlling bloat, dubbed spatial structure with elitism (SS+E), uses a combination of spatial population structure and local elitist replacement to implicitly constrain unwarranted program growth. However, the default implementation of SS+E uses a replacement scheme that prevents the introduction of smaller programs in the presence of equal fitness. This paper introduces a modified SS+E approach in which replacement is done under a lexicographic parsimony scheme. The proposed model, spatial structure with lexicographic parsimonious elitism (SS+LPE), exhibits an improvement in bloat reduction and, in some cases, more effectively searches for fitter solutions.