Standard Genetic Algorithm

A genetic algorithm is an optimization method inspired by evolution and survival of the fittest. A trial solution to the problem is constructed in the form of a suitably encoded string of model parameters, called an individual. A collection of individuals is in turn called a population. There are several considerations and choices to be made in order to implement a suitable solution to an optimization problem using genetic algorithms. A full description of all the practical details is outside the scope of this paper, and some of them are a matter of active research Beasley et al. (1993); Falkenauer (1998); Gen and Cheng (2000); Haupt and Haupt (1998). In Appendix A I give a brief description of the most relevant issues of genetic algorithm optimization as used in this study. In particular, I describe model-parameter encoding as well as standard and non-standard operators (selection, jump and creep mutation, crossover, elitism and niching), fitness function and convergence criteria.