title: Constrained neural approaches to quadratic assignment problems.
abstract: In this paper, we discuss analog neural approaches to the quadratic assignment problem (QAP). These approaches employ a hard constraints scheme to restrict the domain space, and are able to obtain much improved solutions over conventional neural approaches. Since only a few strong heuristics for QAP have been known to date, our approaches are good alternatives, capable of obtaining fairly good solutions in a short period of time. Some of them can also be applied to large-scale problems, say of size N>/=300.
authors.affiliation: Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma-shi, Nara, Japan
authors.forename: S
authors.initials: S
authors.lastname: Ishii
authors.forename: M
authors.initials: M
authors.lastname: Sato