VTechWorks staff will be away for the Memorial Day holiday on Monday, May 27, and will not be replying to requests at that time. Thank you for your patience.
A Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations
Gantovnik, Vladimir B.
Watson, Layne T.
Anderson-Cook, Christine M.
MetadataShow full item record
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.