A HYBRID GENETIC ALGORITHM FOR A TYPE OF NONLINEAR-PROGRAMMING PROBLEM

Based on the introduction of some new concepts of semifeasible directi on, Feasible Degree (FD1) of semifeasible direction, feasible degree ( FD2) of illegal points 'belonging to' feasible domain, etc., this pape r proposed a new fuzzy method for formulating and evaluating illegal p oints and three new kinds of evaluation functions and developed a spec ial 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 ar ithmetic combinatorial crossover only in the later generation process. Simulation of some examples show that this method is effective. (C) 1 998 Elsevier Science Ltd. All rights reserved.