A genetic algorithm with memory for mixed discrete-continuous design optimization
2003) A genetic algorithm with memory for mixed discrete-continuous design optimization. Technical Report TR-03-12, Computer Science, Virginia Tech. (
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.
|Item Type:||Departmental Technical Report|
|Keywords:||Genetic Algorithm, Composite Structure, Response Surface Approximation|
|Subjects:||Computer Science > Parallel Computation|
|Deposited By:||Administrator, Eprints|
|Deposited On:||24 August 2005|