This paper presents an application of genetic algorithms (GAs) to nonlinear constrained optimization. GAs are general purpose optimization algorithms which apply the rules of natural genetics to explore a given search space. When GAs are applied to nonlinear constrained problems, constraint handling becomes an important issue. The proposed search algorithm is realized by GAs which utilize a penalty function in the objective function to account for violation. This extension is based on systematic multi-stage assignments of weights in the penalty method as opposed to single-stage assignments in sequential unconstrained minimization. The experimental results are satisfactory and agree well with those of the gradient type methods.
|頁（從 - 到）||242-254|
|出版狀態||Published - 1994 四月 1|
All Science Journal Classification (ASJC) codes