Improved genetic algorithm for nonlinear programming problems

An improved genetic algorithm (IGA) based on a novel selection strategy to handle nonlinear programming problems is proposed. Each individual in selection process is represented as a three-dimensional feature vector which is composed of objective function value, the degree of constraints violations and the number of constraints violations. It is easy to distinguish excellent individuals from general individuals by using an individuals' feature vector. Additionally, a local search (LS) process is incorporated into selection operation so as to find feasible solutions located in the neighboring areas of some infeasible solutions. The combination of IGA and LS should offer the advantage of both the quality of solutions and diversity of solutions. Experimental results over a set of benchmark problems demonstrate that IGA has better performance than other algorithms.