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 size and shape
Advances in genetic programming
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
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Evolution of robustness in digital organisms
Artificial Life
Resilient Individuals Improve Evolutionary Search
Artificial Life
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
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Previous research has shown that evolutionary systems not only try to develop solutions that satisfy a fitness requirement, but indirectly attempt to develop genetically robust solutions as well -solutions where average loss of fitness due to crossover and other genetic variation operators is minimized. It has been shown that in a simple "two peaks" problem, where the fitness landscape consists of a broad, low peak, and a narrow, high peak, individuals initially converge on the lower (less fit), but broader peak, and that increasing an individual's genetic robustness through growth is a necessary prerequisite for convergence on the higher, narrower peak 18. If growth is restricted, the population remains converged on the less fit solution. We tested whether this result holds true only for generational algorithms, or whether it applies to steady state algorithms as well. We conclude that although growth occurs with both algorithms, the steady state algorithm is able to converge on the higher peak without this growth. This result shows that the role of genetic robustness in the evolutionary process is significantly different in generational versus steady state algorithms.