An educational genetic algorithms learning tool

• Authors: Ying-Hong Liao;Chuen-Tsai Sun
• Venue: IEEE Transactions on Education
• Year: 2001

During the last thirty years, there has been a rapidly growing interest in a field called genetic algorithms (GAs). The field is at a stage of tremendous growth as evidenced by the increasing number of conferences, workshops and papers concerning it, as well as the emergence of a central journal for the field. With their great robustness, genetic algorithms have proven to be a promising technique for many optimization, design, control, and machine learning applications. Students who take a GAs course study and implement a wide range of difference techniques of GAs. And practical implementation experience plays a very important role in learning computer relative courses. Herein, an educational genetic algorithm learning tool (EGALT) has been developed to help students facilitate GAs course. With the readily available tool students can reduce the mechanical programming aspect of learning and concentrate on principles alone. A friendly graphic user interface was established to help students operate and control not only the structural identification but also the parametric identification of GAs. It outlines how to implemented genetic algorithms, how to set parameters of different kinds of problems, and recommends a set of genetic algorithms, which were suggested in previous studies