1. Visualization of a Simple Genetic Algorithm for Pedagogical Purposes Vedrana Vidulin Bogdan Filipič Jožef Stefan Institute, Department of Intelligent Systems vedrana.vidulin@ijs.si bogdan.filipic@ijs.si

2. Motivation: –To facilitate the explanation of how genetic algorithms work. SGA Algorithm: –Based on the Simple Genetic Algorithm described in [Goldberg, 1989].

3. Problem Solved by SGA (1) 0 1 0 1 1 0 1 1 0 0 2 2 + 2 3 + 2 5 + 2 6 + 2 8 364

4. Problem Solved by SGA (2) Fitness function Coefficient Generation consisted of 10 solutions Roulette-wheel selection

5. Forms of Graphical Representation Colored StringsColored Strings Graphical Representation of Best-so-far Fitness Statistics

6. Program Functions Inputs Actions

7. Recommended GA Sources A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Springer, 2003 Genetic Algorithm – Wikipedia, 2006, http://en.wikipedia.org/wiki/Genetic_algorithm D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989 R. L. Haupt, S. E. Haupt, Practical Genetic Algorithms, 2nd Edition, Wiley-Interscience, 2004 M. Obitko, P. Slavik, Visualization of genetic algorithms in a learning environment, Spring Conference on Computer Graphics SCCG'99, Comenius University, Bratislava, p. 101-106, 1999 R. E. Smith, D. E. Goldberg, J. A. Earickson, SGA-C: A C-language Implementation of a Simple Genetic Algorithm, The Clearinghouse for Genetic Algorithms, Technical Report No. 91002, University of Alabama, Department of Engineering Mechanics, Tuscaloosa 1994