An improved genetic algorithm based on a novel selection strategy for nonlinear programming problems

Genetic algorithm is a heuristic population-based search method that incorporates three primary operators: crossover, mutation and selection. Selection operator plays a crucial role in finding optimal solution for constrained optimization problems. In this paper, an improved genetic algorithm (IGA) based on a novel selection strategy is presented to handle nonlinear programming problems. Each individual in selection process is represented as a three-dimensional feature vector composed of objective function value, the degree of constraints violations and the number of constraints violations. We can distinguish excellent individuals through two indices according to Pareto partial order. Additionally, IGA incorporates a local search (LS) process into selection operation so as to find feasible solutions located in neighboring areas of some infeasible solutions. Experimental results over a set of benchmark problems demonstrate that proposed IGA has better robustness, effectiveness and stableness than other algorithm reported in literature.