Abstract

In this paper we discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP ) using real-valued GAs. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas presented in this paper come from two basic areas: calculus-based nonlinear programming and simulated annealing. The non-stationary penalty methods are tested on four NP test cases and the effectiveness of these methods are reported.. 1 Introduction Constrained function optimization is an extremely important tool used in almost every facet of engineering, operations research, mathematics, and etc. Constrained optimization can be represented as a nonlinear programming problem. The general nonlinear programming problem is defined as follows: (NP ) minimize f(X) subject to (nonlinear and linear)...

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