“Genetic algorithm” is one of the optimization techniques whereby the principles of genetics and evolution of living organisms are mathematically modeled and global optimization problems are solved based on them.
In the past, teaching genetic algorithms mainly focused on mathematical basis, principles and algorithmic description, and so it revealed that students had difficulty in selecting the best one of all different methods used to solve a problem.
To solve this problem, Kang Kum Sik, a lecturer at the Faculty of Applied Mathematics, let his students to write genetic algorithms and compare and analyse them.
Firstly, he encouraged the students to decide by themselves which methods are the best among different selection methods, crossover methods, and mutation methods used in each genetic algorithm, thereby enhancing their analytical and practical abilities.
Secondly, he led the students to synthesize the relationship of each genetic algorithm and find a genetic algorithm best suited for solving real-world problems, thus raising their synthesizing and systemizing ability and the creative ability of finding optimal solutions.
That is how students came to fully understand what they learn in both theoretical and programmatic ways, and acquire methods of finding optimal solutions while solving practical problems.
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