Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Evolutionary Computation
Walsh transforms, balanced sum theorems and partition coefficients over multary alphabets
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Matrix interpretation of generalized embedded landscape
Proceedings of the 9th annual conference on Genetic and evolutionary computation
General cardinality genetic algorithms
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Detecting the epistatic structure of generalized embedded landscape
Genetic Programming and Evolvable Machines
Matrix interpretation of generalized embedded landscape
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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The work addresses the problem of identifying the epistatic linkage of a function from high cardinality alphabets to the real numbers. It is a generalization of Heckendorn and Wright's work that restricts problem representation into the binary-string domain. Discrete Fourier transform is used to analyze underlying structure in high-cardinality alphabets space. Boolean operators are replaced with new operators such as ⊕, ⊖, ⊗ and so on in high cardinality alphabets. The "probe" formulation is redesigned to determine epistatic properties of non-binary function. Theoretical analysis shows the close relationship between probe value and problem structure. A deterministic and a stochastic algorithm based on properties of probes are proposed to completely identify the linkage structure and rigourously compute all Fourier coefficients. Some discussion about linkage detection for continuous problems is given.