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
The evolution of size and shape
Advances in genetic programming
An Analysis of the Causes of Code Growth 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 in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The root causes of code growth in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Foundations of Genetic Programming
Foundations of Genetic Programming
Genetic Programming and Evolvable Machines
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
The identification and exploitation of dormancy in genetic programming
Genetic Programming and Evolvable Machines
The influence of mutation on population dynamics in multiobjective genetic programming
Genetic Programming and Evolvable Machines
Operator equalisation for bloat free genetic programming and a survey of bloat control methods
Genetic Programming and Evolvable Machines
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The authors employ multiple crossovers as a novel natural extension to crossovers as a mixing operator. They use this as a framework to explore the ideas of code growth. Empirical support is given for popular theories for mechanisms of code growth. Three specific algorithms for multiple crossovers are compared with classic methods for performance in terms of fitness and genome size. The details of the performance of these algorithms is examined in detail for both practical value and theoretical implications. The authors conclude that multiple crossovers is a practical scheme for containing code growth without a significant loss of fitness.