Work

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

Year: 2005

Type: article

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.

Source: AIAA Journal

DOI: 10.2514/1.4191

Authors: Vladimir B. Gantovnik, Zafer Gurdal, Layne T. Watson, Christine M. Anderson-Cook

Institution: Virginia Tech

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Air Force Office of Scientific Research

Award: Mathematical Sciences: Theory and Application of Homotopy Techniques in Nonlinear Programming