Computer Science Technical Reports
CS at VT

A Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations

Gantovnik, Vladimir B. and Gurdal, Zafer and Watson, Layne T. and Anderson-Cook, Christine M. (2003) A Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations. Technical Report TR-03-22, Computer Science, Virginia Tech.

Full text available as:
PDF - Requires Adobe Acrobat Reader or other PDF viewer.
gaAIAAJ03.pdf (159584)

Abstract

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation.The additions involve memory as a function of both discrete and continuous design variables, and multivariate approximation of the individual functions' responses in terms of several continuous design variables. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.

Item Type:Departmental Technical Report
Keywords:Genetic Algorithm
Subjects:Computer Science > Parallel Computation
ID Code:667
Deposited By:Administrator, Eprints
Deposited On:26 August 2005