Titlebook: Estimation of Distribution Algorithms; A New Tool for Evolu Pedro Larra?aga,Jose A. Lozano Book 2002 Springer Science+Business Media New Yo

Thyroid-Gland

Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms for the Nearest Neighbor algorithm. While the FW-EBNA has a set of three possible discrete weights, the FW-EGNA works in a continuous range of weights. Both methods are compared in a set of natural and artificial domains with two sequential and one Genetic Algorithm.

Vital-Signs

Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAscessfully applied to give an approximate algorithm for it (de Campos et al., 1999). In this work we approach the problem by means of Estimation of Distribution Algorithms, and an empirical comparison between the results obtained by Genetic Algorithms and Estimation of Distribution Algorithms is carried out.

災(zāi)難

Matrimony

Solving the Traveling Salesman Problem with EDAsarch) is combined with EDAs to find better solutions. We show experimental results obtained on several standard examples for discrete and continuous EDAs both alone and combined with a heuristic local search.

Adenoma

Rule Induction by Estimation of Distribution Algorithmsmple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UMDA, a dependency tree and EBNAwith two classical algorithms of rule induction —RIPPER and CN2— are shown.

Brain-Imaging

DNR215

Promotion

M. Kasaya,K. Takegahara,A. Yanase,T. Kasuya for the Nearest Neighbor algorithm. While the FW-EBNA has a set of three possible discrete weights, the FW-EGNA works in a continuous range of weights. Both methods are compared in a set of natural and artificial domains with two sequential and one Genetic Algorithm.

誘惑

釋放