Comparison of adaptive genetic algorithms for engineering optimization problems

In this paper, we propose various adaptive genetic algorithms (AGAs) for effectively solving engineering optimization problems. To construct the AGAs, two adaptive schemes drawn from conventional works and one adaptive scheme using the fuzzy logic controller improved in this paper are respectively considered. These adaptive schemes are able to regulate the rates of crossover and mutation operators of genetic algorithms. In test problem using engineering optimization problems, the AGAs are tested and analyzed. Finally, a best algorithm is recommended.