Genetic algorithms for nonlinear mixed discrete-integer optimization problems via meta-genetic parameter optimization

This paper describes the application of genetic algorithms to nonlinear constrained mixed discrete integer optimization problems with optimal sets of parameters furnished by a meta-genetic algorithm. Genetic algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. Careful attention has been paid to modify the genetic algorithms to promote computational efficiency. Some numerical experiments were performed so as to determine the appropriate range of genetic parameter values. Then the meta-genetic algorithm was employed to optimize these parameters to locate the best solution. Three examples are given to demonstrate the effectiveness of the methodology developed in this paper. Four crossover operators have been compared and the results show that a four-point crossover operator performs best.