Difficulty of linkage learning in estimation of distribution algorithms
Journal
11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Pages
397-403
Date Issued
2009
Author(s)
Chen, S.-C.
Abstract
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.
Type
conference paper