A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on 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
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Does overfitting affect performance in estimation of distribution algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Hierarchical allelic pairwise independent functions
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Higher-order linkage learning in the ECGA
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A linkage-learning niching in estimation of distribution algorithm
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Linkage learning by number of function evaluations estimation: Practical view of building blocks
Information Sciences: an International Journal
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This paper investigates the difficulty of linkage learning, an essential core, in EDAs. Specifically, it examines allelicpairwise independent functions including the parity, paritywith-trap, and Walsh-code functions. While the parity function was believed to be difficult for EDAs in previous work, our experiments indicate that it can be solved by CGA within a polynomial number of function evaluations to the problem size. Consequently, the apparently difficult paritywith-trap function can be easily solved by ECGA, even though the linkage model is incorrect. A convergence model for CGA on the parity function is also derived to verify and support the empirical findings. Finally, this paper proposes a socalled Walsh-code function, which is more difficult than the parity function. Although the proposed function does deceive the linkage-learning mechanism in most EDAs, EDAs are still able to solve it to some extent.