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
Number Theory for Computing
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolutionary Computation
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
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In this paper, embedded landscapes are extended to a non-binary discrete domain. Generalized embedded landscapes (GEL) are a class of additive decomposable problems where the representation can be expressed as a simple sum of subfunctions over subsets of the representation domain. The paper proposes a Generalized Embedding Theorem that reveals the close relationship between the underlying structure and the Walsh coefficients. Theoretical inductions show that the Walsh coefficients of any GEL with bounded difficulty can be calculated with a polynomial number of function evaluations. A deterministic algorithm is proposed to construct the decomposed representation of GEL. It offers an efficient way to detect the decomposable structure of the search space.