title: Convergence time for the linkage learning genetic algorithm.
abstract: This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.
authors.affiliation: Department of Computer Science, National Chiao Tung University, Hsinchu City 300, Taiwan. ypchen@csie.nctu.edu.tw
authors.forename: Ying-ping
authors.initials: YP
authors.lastname: Chen
authors.forename: David E
authors.initials: DE
authors.lastname: Goldberg
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D005823:Genetics; D007858:Learning