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A genetic algorithm with memory for mixed discrete-continuous design optimization |
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| Authors: | Vladimir B. Gantovnik,Christine M. Anderson-CookZafer Gü rdal,Layne T. Watson |
| Affiliation: | a Department of Engineering Science and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA b Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA c Departments of Aerospace and Ocean Engineering, and Engineering Science and Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA d Departments of Computer Science, and Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA |
| Abstract: | This paper describes a new approach for reducing the number of the fitness 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, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners. |
| Keywords: | Optimization Genetic algorithm Response surface approximation Composite structure |
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