This paper presents a particle swarm optimizer for solving constrained optimization problems which adopts a very small population size (five particles). The proposed approach uses...
Juan Carlos Fuentes Cabrera, Carlos A. Coello Coel...
It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase ordering problem ...
Prasad Kulkarni, Stephen Hines, Jason Hiser, David...
Background: To infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching s...
Background: In a previous report (La et al., Proteins, 2005), we have demonstrated that the identification of phylogenetic motifs, protein sequence fragments conserving the overal...
Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clu...