Trusses, NP‐Completeness, and Genetic Algorithms

Overbay, Shannon; Ganzerli, Sara; De Palma, Paul; Brown, Aaron; Stackle, Peter

doi:doi:10.1061/40878(202)44

Search Content Type

Search
Advanced Search

You are not logged in to this journal. Log In

Proceedings / Volume 202 / Issue 40878 / INNOVATIVE COMPUTATIONAL METHODS

Buy PDF (US$25)
Permissions

Trusses, NP‐Completeness, and Genetic Algorithms

ASCE Conf. Proc. doi:http://dx.doi.org/10.1061/40878(202)44

17th Analysis and Computation Specialty Conference

Proceedings of the Conference

Shannon Overbay¹, Sara Ganzerli², Paul De Palma³, Aaron Brown⁴, and Peter Stackle⁵

¹Department of Mathematics and Computer Science, Gonzaga University, Spokane, WA 99258‐2615, overbay@gonzaga.edu
²Department of Civil Engineering, Gonzaga University, Spokane, WA 99258‐0026, ganzerli@gonzaga.edu
³Department of Mathematics and Computer Science, Gonzaga University, Spokane, WA 99258‐2615, depalma@gonzaga.edu
⁴Department of Mathematics and Computer Science, Gonzaga University, Spokane, WA 99258‐2615, abrown2@gonzaga.edu
⁵Department of Mathematics and Computer Science, Gonzaga University, Spokane, WA 99258‐2615, pstackle@gonzaga.edu

Alerts
- Alert Me When Cited
- Alert Me When Corrected
Tools
Share
- Email Abstract
- Connotea
- CiteULike
- del.icio.us
- BibSonomy
- Tweet this Article
- Add to Facebook

Abstract

The optimization of large trusses often leads to a nearly optimal solution, rather than a truly optimal design. In fact, the problem space for truss optimization grows exponentially with the size of the truss. Using the method of problem reduction, this paper demonstrates that truss optimization is in the set of NP‐complete problems. Hence, the only practical techniques for solving the truss problem are heuristic in nature. Genetic algorithms provide a viable solution for large trusses.