As an extension of the hybrid Genetic Algorithm-HGA proposed by Tang et al., this paper focuses on the critical techniques in the application of the GA to nonlinear programming problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible points/chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are proposed to formulate and evaluate the infeasible chromosomes. The extended version of concepts of dominated semi-feasible direction, feasibility degree of semi-feasible direction, feasibility degree of infeasible points ‘belonging to’ feasible domain are introduced. Combining the new evaluation functions and weighted gradient direction search into the Genetic Algorithm, an extended hybrid Genetic Algorithm is developed to solve nonlinear programming problems with equality and inequality constraints. Simulation shows that this new algorithm is efficient.
