Convergence rates for Markov chains
SIAM Review
A computational view of population genetics
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Theoretical Computer Science
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Group properties of crossover and mutation
Evolutionary Computation
Proceedings of the European Conference on Genetic Programming
Structural Search Spaces and Genetic Operators
Evolutionary Computation
Crossover Invariant Subsets of the Search Space for Evolutionary Algorithms
Evolutionary Computation
An Extension of Geiringer's Theorem for a Wide Class of Evolutionary Search Algorithms.
Evolutionary Computation
Comparing evolutionary computation techniques via their representation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Understanding the Biases of Generalised Recombination: Part II
Evolutionary Computation
Genetic Programming and Evolvable Machines
The halting probability in von neumann architectures
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Natural Computing: an international journal
Hi-index | 0.00 |
Geiringer's theorem is a statement which tells us something about the limiting frequency of occurrence of a certain individual when a classical genetic algorithm is executed in the absence of selection and mutation. Recently Poli, Stephens, Wright and Rowe extended the original theorem of Geiringer to include the case of variable length genetic algorithms and linear genetic programming. In the current paper a rather powerful version of Geiringer's theorem which has been established recently by Mitavskiy is used to derive a schema-based version of the theorem for nonlinear genetic programming with homologous crossover.