Constrained genetic algorithms and their applications in nonlinear constrained optimization

This paper presents a problem-independent framework that uni es various mechanisms for solving discrete constrained nonlinear programming (NLP) problems whose functions are not necessarily di erentiable and continuous. The framework is based on the rst-order necessary and su cient conditions in the theory of discrete constrained optimization using Lagrange multipliers. It implements the search for discrete-neighborhood saddle points (SPdn) by performing ascents in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our study on the various mechanisms shows that CSAGA, a combined constrained simulated annealing and genetic algorithm, performs well. Finally, we apply iterative deepening to determine the optimal number of generations in CSAGA.