Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Group properties of crossover and mutation
Evolutionary Computation
Structural Search Spaces and Genetic Operators
Evolutionary Computation
Differentiable coarse graining
Theoretical Computer Science - Foundations of genetic algorithms
Geometric crossovers for multiway graph partitioning
Evolutionary Computation
Neighborhood graphs and symmetric genetic operators
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Unbiased black box search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Quotient geometric crossovers and redundant encodings
Theoretical Computer Science
A distance between populations for one-point crossover in genetic algorithms
Theoretical Computer Science
Representations for evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Representations for evolutionary algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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A genetic algorithm is invariant with respect to a set of representations if it runs the same no matter which of the representations is used. We formalize this concept mathematically, showing that the representations generate a group that acts upon the search space. Invariant genetic operators are those that commute with this group action. We then consider the problem of characterizing crossover and mutation operators that have such invariance properties. In the case where the corresponding group action acts transitively on the search space, we provide a complete characterization, including high-level representation-independent algorithms implementing these operators.