A hybrid genetic algorithm for a type of nonlinear programming problem

Based on the introduction of some new concepts of semifeasible direction, Feasible Degree (FD1) of semifeasible direction, feasible degree (FD2) of illegal points ‘belonging to’ feasible domain, etc., this paper proposed a new fuzzy method for formulating and evaluating illegal points and three new kinds of evaluation functions and developed a special Hybrid Genetic Algorithm (HGA) with penalty function and gradient direction search for nonlinear programming problems. It uses mutation along the weighted gradient direction as its main operator and uses arithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective.