Computational experiments with genetic algorithms applied to set covering problems

Genetic algorithms (GA) are a class of iteration procedures that simulate the evolution process of a structured population. The objective of this work is to report computational experiments with a somewhat classical GA implementation used to solve set covering problems. Algorithms were tested with problems from the literature (dimensions 200 × 1000 to 300 × 3000) and difficult problems arising in computing the 1-width of incidence matrices of Steiner triple systems. Computational results are of high quality with reasonable times for a microcomputer implementation.