Solving constrained optimization problems using a novel genetic algorithm

A novel genetic algorithm is described in this paper for the problem of constrained optimization. The algorithm incorporates modified genetic operators that preserve the feasibility of the trial solutions encoded in the chromosomes, the stochastic application of a local search procedure and a stopping rule which is based on asymptotic considerations. The algorithm is tested on a series of well-known test problems and a comparison is made against the algorithms C-SOMGA and DONLP2.